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Search Results (1,213)

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Keywords = prefrontal cortex

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9 pages, 1644 KiB  
Article
The Development of Ambiguity Processing Is Explained by an Inverted U-Shaped Curve
by Anna Manelis, Rachel Miceli, Skye Satz, Stephen J. Suss, Hang Hu and Amelia Versace
Behav. Sci. 2024, 14(9), 826; https://doi.org/10.3390/bs14090826 (registering DOI) - 16 Sep 2024
Viewed by 233
Abstract
Understanding the developmental trajectories for recognizing facial expressions is important for a better understanding of development of psychiatric disorders. In this study, we examined the recognition of emotional and neutral facial expressions in 93 typically developing adolescents and adults. The Emotion Intensity Rating [...] Read more.
Understanding the developmental trajectories for recognizing facial expressions is important for a better understanding of development of psychiatric disorders. In this study, we examined the recognition of emotional and neutral facial expressions in 93 typically developing adolescents and adults. The Emotion Intensity Rating task required participants to rate the intensity of emotional expression in happy, neutral, and sad faces on a scale from 1 to 9. A score of ‘5’ had to be assigned to neutral faces, scores between ‘6’ (slightly happy) and ‘9’ (very happy) to happy faces, and scores between ‘4’ (slightly sad) and ‘1’ (very sad) to sad faces. Mixed effects models were used to examine the effects of age and emotion on recognition accuracy, reaction time (RT), and emotional intensity. Participants tended to misjudge neutral faces as sad. Adolescents were less accurate than adults for neutral face recognition. There were significant quadratic effects of age on accuracy (negative quadratic effect) and RT (positive quadratic effect). The most accurate and fastest performance was observed in 25- to 35-year-old subjects. This trajectory may be associated with prefrontal cortex maturation, which provides top–down control over the heightened amygdala response to ambiguity that may be misinterpreted as emotional content. Full article
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Figure 1
<p>Examples of sad, neutral, and happy face trials in the Emotion Intensity Rating task. The images 40M_SA_O, 01F_NE_C, and 18F_HA_O are taken from the NimStim dataset [<a href="#B36-behavsci-14-00826" class="html-bibr">36</a>]. The correct responses for the sad facial expression are between ‘1’ and ‘4’. The correct response to the neutral facial expression is ‘5’. The correct response to the happy facial expression is between ‘6’ and ‘9’.</p>
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<p>Accuracy, intensity ratings, and RT in adolescent (13–17 yo) and adult (18–45 yo) male and female participants. Standard error bars represent standard errors, estimated from the mixed effects model. F—female, M—male.</p>
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<p>The effects of age and sex on accuracy (<b>A</b>) and RT (<b>B</b>) for recognizing neutral facial expressions.</p>
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16 pages, 1722 KiB  
Article
Functional Connectome Controllability in Patients with Mild Cognitive Impairment after Repetitive Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex
by Simone Papallo, Federica Di Nardo, Mattia Siciliano, Sabrina Esposito, Fabrizio Canale, Giovanni Cirillo, Mario Cirillo, Francesca Trojsi and Fabrizio Esposito
J. Clin. Med. 2024, 13(18), 5367; https://doi.org/10.3390/jcm13185367 - 10 Sep 2024
Viewed by 358
Abstract
Background: Repetitive transcranial magnetic stimulation (rTMS) has shown therapeutic effects in neurological patients by inducing neural plasticity. In this pilot study, we analyzed the modifying effects of high-frequency (HF-)rTMS applied to the dorsolateral prefrontal cortex (DLPFC) of patients with mild cognitive impairment [...] Read more.
Background: Repetitive transcranial magnetic stimulation (rTMS) has shown therapeutic effects in neurological patients by inducing neural plasticity. In this pilot study, we analyzed the modifying effects of high-frequency (HF-)rTMS applied to the dorsolateral prefrontal cortex (DLPFC) of patients with mild cognitive impairment (MCI) using an advanced approach of functional connectome analysis based on network control theory (NCT). Methods: Using local-to-global functional parcellation, average and modal controllability (AC/MC) were estimated for DLPFC nodes of prefrontal-lateral control networks (R/LH_Cont_PFCl_3/4) from a resting-state fMRI series acquired at three time points (T0 = baseline, T1 = T0 + 4 weeks, T2 = T1 + 20 weeks) in MCI patients receiving regular daily sessions of 10 Hz HF-rTMS (n = 10, 68.00 ± 8.16 y, 4 males) or sham (n = 10, 63.80 ± 9.95 y, 5 males) stimulation, between T0 and T1. Longitudinal (group) effects on AC/MC were assessed with non-parametric statistics. Spearman correlations (ρ) of AC/MC vs. neuropsychological (RBANS) score %change (at T1, T2 vs. T0) were calculated. Results: AC median was reduced in MCI-rTMS, compared to the control group, for RH_Cont_PFCl_3/4 at T1 and T2 (vs. T0). In MCI-rTMS patients, for RH_Cont_PFCl_3, AC % change at T1 (vs. T0) was negatively correlated with semantic fluency (ρ = −0.7939, p = 0.045) and MC % change at T2 (vs. T0) was positively correlated with story memory (ρ = 0.7416, p = 0.045). Conclusions: HF-rTMS stimulation of DLFC nodes significantly affects the controllability of the functional connectome in MCI patients. Emerging correlations between AC/MC controllability and cognitive performance changes, immediately (T1 vs. T0) and six months (T2 vs. T0) after treatment, suggest NCT could help explain the HF-rTMS impact on prefrontal-lateral control network, monitoring induced neural plasticity effects in MCI patients. Full article
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Figure 1

Figure 1
<p>Localization of the stimulation site. Resorting to the 100-region Schaefer parcellation, we identified the functional connectome nodes encompassing the nominal stimulation site (DLPFC). The LH_Cont_PFCl_1 (Yellow), RH_Cont_PFCl_1 (Orange), RH_Cont_PFCl_2 (Green), RH_Cont_PFCl_3 (Blue), and RH_Cont_PFCl_4 (Red) are here reported in the sagittal, coronal, and transversal planes.</p>
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<p>The boxplots of AC estimates for both groups (in light blue, MCI-C; in red, MCI-TMS; the black points for the AC estimates of each subject) across time points (T0, T1, and T2) for the five regions, namely, LH_Cont_PFCl_1 (<b>a</b>), RH_Cont_PFCl_1 (<b>b</b>), RH_Cont_PFCl_2 (<b>c</b>), RH_Cont_PFCl_3 (<b>d</b>), and RH_Cont_PFCl_4 (<b>e</b>). The two-way ANOVA with one within factor (observation time) and one between factors (group membership) showed that the AC was significantly affected by the interaction term, i.e., the interplay between the within and between factors, in RH_Cont_PFCl_3 (F (2, 36) = 5.09, <span class="html-italic">p</span> = 0.01), as reported in the subtitle of the corresponding plot. The Mann–Whitney test (U) as a post hoc test was performed, but no significant difference between the groups at each time point was obtained. In no other selected regions did the interaction term result as statistically significant. FDR correction for multiple comparisons was considered.</p>
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<p>The boxplots of AC estimates (the black dots) across time points (T0, T1 and T2) for the MCI-C (in light blue) and for the MCI-TMS (in red) are here reported separately for the RH_Cont_PFCl_3, where the repeated measure ANOVA was significant, when considering the MCI-TMS group, as reported in the subtitle. The Wilcoxon signed-rank test was performed to evaluate short- and long-term changes of the AC estimates of the considered region. Short- and long-term statistically significant reduction (<span class="html-italic">p</span> &lt; 0.05) for the AC estimates was found (after FDR correction, * <span class="html-italic">p</span> &lt; 0.05) in the MCI-TMS group but not in MCI-C group.</p>
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<p>The boxplots of MC estimates (the black dots) for both group (in light blue MCI-C, in red MCI-TMS, the black points for the AC estimates of each subject) across time points (T0, T1 and T2) for the five regions, namely, LH_Cont_PFCl_1 (<b>a</b>), RH_Cont_PFCl_1 (<b>b</b>), RH_Cont_PFCl_2 (<b>c</b>), RH_Cont_PFCl_3 (<b>d</b>), and RH_Cont_PFCl_4 (<b>e</b>). In no cases, the two-way ANOVA with one within factor (observation time) and one between factors (group membership) showed MC estimates significantly affected by the interaction term, i.e., the interplay between the within and between factors. The Mann–Whitney test (U) as post-hoc test was performed but no significant difference between the groups at each time points was obtained (ns, <span class="html-italic">p</span> &gt; 0.05).</p>
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22 pages, 6422 KiB  
Systematic Review
Metaanalysis of Repetitive Transcranial Magnetic Stimulation (rTMS) Efficacy for OCD Treatment: The Impact of Stimulation Parameters, Symptom Subtype and rTMS-Induced Electrical Field
by Fateme Dehghani-Arani, Reza Kazemi, Amir-Homayun Hallajian, Sepehr Sima, Samaneh Boutimaz, Sepideh Hedayati, Saba Koushamoghadam, Razieh Safarifard and Mohammad Ali Salehinejad
J. Clin. Med. 2024, 13(18), 5358; https://doi.org/10.3390/jcm13185358 - 10 Sep 2024
Viewed by 671
Abstract
Background: Repetitive transcranial magnetic stimulation (rTMS) has recently demonstrated significant potential in treating obsessive-compulsive disorder (OCD). However, its effectiveness depends on various parameters, including stimulation parameters, OCD subtypes and electrical fields (EFs) induced by rTMS in targeted brain regions that are less [...] Read more.
Background: Repetitive transcranial magnetic stimulation (rTMS) has recently demonstrated significant potential in treating obsessive-compulsive disorder (OCD). However, its effectiveness depends on various parameters, including stimulation parameters, OCD subtypes and electrical fields (EFs) induced by rTMS in targeted brain regions that are less studied. Methods: Using the PRISMA approach, we examined 27 randomized control trials (RCTs) conducted from 1985 to 2024 using rTMS for the treatment of OCD and conducted several meta-analyses to investigate the role of rTMS parameters, including the EFs induced by each rTMS protocol, and OCD subtypes on treatment efficacy. Results: A significant, medium effect size was found, favoring active rTMS (gPPC = 0.59, p < 0.0001), which was larger for the obsession subscale. Both supplementary motor area (SMA) rTMS (gPPC = 0.82, p = 0.048) and bilateral dorsolateral prefrontal cortex (DLPFC) rTMS (gPPC = 1.14, p = 0.04) demonstrated large effect sizes, while the right DLPFC showed a significant moderate effect size for reducing OCD severity (gPPC = 0.63, p = 0.012). These protocols induced the largest EFs in dorsal cognitive, ventral cognitive and sensorimotor circuits. rTMS protocols targeting DLPFC produced the strongest electrical fields in cognitive circuits, while pre-supplementary motor area (pre-SMA) and orbitofrontal cortex (OFC) rTMS protocols induced larger fields in regions linked to emotional and affective processing in addition to cognitive circuits. The pre-SMA rTMS modulated more circuits involved in OCD pathophysiology—sensorimotor, cognitive, affective, and frontolimbic—with larger electrical fields than the other protocols. Conclusions: While rTMS shows moderate overall clinical efficacy, protocols targeting ventral and dorsal cognitive and sensorimotor circuits demonstrate the highest potential. The pre-SMA rTMS appears to induce electrical fields in more circuits relevant to OCD pathophysiology. Full article
(This article belongs to the Special Issue Neuro-Psychiatric Disorders: Updates on Diagnosis and Treatment)
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Figure 1
<p>Flowchart diagram of the study selection process.</p>
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<p>The distribution of the induced normed electrical field for each rTMS protocol.</p>
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<p>(<b>A</b>) Pooled effect sizes (g<sub>ppc</sub>) of rTMS studies for reducing OCD symptoms. (<b>B</b>) Pooled effect sizes (gppc) of rTMS studies for reducing obsession symptoms (<b>C</b>) Pooled effect sizes (g<sub>ppc</sub>) of rTMS studies for reducing compulsion symptoms, CI: confidence interval, SMD: standardized mean difference [<a href="#B54-jcm-13-05358" class="html-bibr">54</a>,<a href="#B55-jcm-13-05358" class="html-bibr">55</a>,<a href="#B56-jcm-13-05358" class="html-bibr">56</a>,<a href="#B57-jcm-13-05358" class="html-bibr">57</a>,<a href="#B58-jcm-13-05358" class="html-bibr">58</a>,<a href="#B59-jcm-13-05358" class="html-bibr">59</a>,<a href="#B60-jcm-13-05358" class="html-bibr">60</a>,<a href="#B61-jcm-13-05358" class="html-bibr">61</a>,<a href="#B62-jcm-13-05358" class="html-bibr">62</a>,<a href="#B63-jcm-13-05358" class="html-bibr">63</a>,<a href="#B64-jcm-13-05358" class="html-bibr">64</a>,<a href="#B65-jcm-13-05358" class="html-bibr">65</a>,<a href="#B66-jcm-13-05358" class="html-bibr">66</a>,<a href="#B67-jcm-13-05358" class="html-bibr">67</a>,<a href="#B68-jcm-13-05358" class="html-bibr">68</a>,<a href="#B69-jcm-13-05358" class="html-bibr">69</a>,<a href="#B70-jcm-13-05358" class="html-bibr">70</a>,<a href="#B71-jcm-13-05358" class="html-bibr">71</a>,<a href="#B72-jcm-13-05358" class="html-bibr">72</a>,<a href="#B73-jcm-13-05358" class="html-bibr">73</a>,<a href="#B74-jcm-13-05358" class="html-bibr">74</a>,<a href="#B75-jcm-13-05358" class="html-bibr">75</a>,<a href="#B76-jcm-13-05358" class="html-bibr">76</a>,<a href="#B77-jcm-13-05358" class="html-bibr">77</a>,<a href="#B78-jcm-13-05358" class="html-bibr">78</a>,<a href="#B79-jcm-13-05358" class="html-bibr">79</a>,<a href="#B80-jcm-13-05358" class="html-bibr">80</a>].</p>
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<p>(<b>A</b>) Pooled effect sizes (gppc) of rTMS studies for reducing OCD symptoms based on the cortical target of rTMS, BL: bilateral, L: left, R: right, DLPFC: dorsolateral prefrontal cortex, OFC: orbitofrontal cortex, SMA: supplementary motor area. (<b>B</b>) Effect sizes (gppc) for OCD symptoms based on the frequency of rTMS. (<b>C</b>) Pooled effect sizes (gppc) for OCD symptoms based on the duration of rTMS treatment. (<b>D</b>) Pooled effect sizes (gppc) for OCD symptoms based on the total induced pulses of rTMS per session, TTPS: total pulse per session. (<b>E</b>) Effect sizes (gppc) for OCD symptoms based on the intensity of rTMS, RMT: resting motor threshold, CI: confidence interval, SMD: standardized mean difference [<a href="#B54-jcm-13-05358" class="html-bibr">54</a>,<a href="#B55-jcm-13-05358" class="html-bibr">55</a>,<a href="#B56-jcm-13-05358" class="html-bibr">56</a>,<a href="#B57-jcm-13-05358" class="html-bibr">57</a>,<a href="#B58-jcm-13-05358" class="html-bibr">58</a>,<a href="#B59-jcm-13-05358" class="html-bibr">59</a>,<a href="#B60-jcm-13-05358" class="html-bibr">60</a>,<a href="#B61-jcm-13-05358" class="html-bibr">61</a>,<a href="#B62-jcm-13-05358" class="html-bibr">62</a>,<a href="#B63-jcm-13-05358" class="html-bibr">63</a>,<a href="#B64-jcm-13-05358" class="html-bibr">64</a>,<a href="#B65-jcm-13-05358" class="html-bibr">65</a>,<a href="#B66-jcm-13-05358" class="html-bibr">66</a>,<a href="#B67-jcm-13-05358" class="html-bibr">67</a>,<a href="#B68-jcm-13-05358" class="html-bibr">68</a>,<a href="#B69-jcm-13-05358" class="html-bibr">69</a>,<a href="#B70-jcm-13-05358" class="html-bibr">70</a>,<a href="#B71-jcm-13-05358" class="html-bibr">71</a>,<a href="#B72-jcm-13-05358" class="html-bibr">72</a>,<a href="#B73-jcm-13-05358" class="html-bibr">73</a>,<a href="#B74-jcm-13-05358" class="html-bibr">74</a>,<a href="#B75-jcm-13-05358" class="html-bibr">75</a>,<a href="#B76-jcm-13-05358" class="html-bibr">76</a>,<a href="#B77-jcm-13-05358" class="html-bibr">77</a>,<a href="#B78-jcm-13-05358" class="html-bibr">78</a>,<a href="#B79-jcm-13-05358" class="html-bibr">79</a>,<a href="#B80-jcm-13-05358" class="html-bibr">80</a>].</p>
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<p>(<b>A</b>) Effect sizes (gppc) for OCD symptoms based on the presence of MDD comorbidity. (<b>B</b>) Effect sizes (gppc) for OCD symptoms based on the strategy of rTMS treatment. (<b>C</b>) Effect sizes (gppc) for OCD symptoms based on the sham stimulation strategy. (<b>D</b>) Effect sizes (gppc) for OCD symptoms based on the blinding strategy. MDD: major depressive disorder, CI: confidence interval, SMD: standardized mean difference [<a href="#B54-jcm-13-05358" class="html-bibr">54</a>,<a href="#B55-jcm-13-05358" class="html-bibr">55</a>,<a href="#B56-jcm-13-05358" class="html-bibr">56</a>,<a href="#B57-jcm-13-05358" class="html-bibr">57</a>,<a href="#B58-jcm-13-05358" class="html-bibr">58</a>,<a href="#B59-jcm-13-05358" class="html-bibr">59</a>,<a href="#B60-jcm-13-05358" class="html-bibr">60</a>,<a href="#B61-jcm-13-05358" class="html-bibr">61</a>,<a href="#B62-jcm-13-05358" class="html-bibr">62</a>,<a href="#B63-jcm-13-05358" class="html-bibr">63</a>,<a href="#B64-jcm-13-05358" class="html-bibr">64</a>,<a href="#B65-jcm-13-05358" class="html-bibr">65</a>,<a href="#B66-jcm-13-05358" class="html-bibr">66</a>,<a href="#B67-jcm-13-05358" class="html-bibr">67</a>,<a href="#B68-jcm-13-05358" class="html-bibr">68</a>,<a href="#B69-jcm-13-05358" class="html-bibr">69</a>,<a href="#B70-jcm-13-05358" class="html-bibr">70</a>,<a href="#B71-jcm-13-05358" class="html-bibr">71</a>,<a href="#B72-jcm-13-05358" class="html-bibr">72</a>,<a href="#B73-jcm-13-05358" class="html-bibr">73</a>,<a href="#B74-jcm-13-05358" class="html-bibr">74</a>,<a href="#B75-jcm-13-05358" class="html-bibr">75</a>,<a href="#B76-jcm-13-05358" class="html-bibr">76</a>,<a href="#B77-jcm-13-05358" class="html-bibr">77</a>,<a href="#B78-jcm-13-05358" class="html-bibr">78</a>,<a href="#B79-jcm-13-05358" class="html-bibr">79</a>,<a href="#B80-jcm-13-05358" class="html-bibr">80</a>].</p>
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<p>(<b>A</b>) Bar plot showing the distribution of risk-of-bias judgments across bias domains. The bars indicate the proportion of studies within each domain, providing an overview of the collective bias risk. The colors represent: low risk (green), some concerns (yellow), and high risk (red). (<b>B</b>) GRADE assessment results. *: Lack of Intention-to-treat analysis in several studies; many didn’t report the allocation concealment procedure (Only 6 studies had done and intention-to-treat analysis 4 of which are in the SMA/pre-SMA group). In addition, the funnel plot shows an asymmetrical pattern suggesting the presence of publication bias. **: 95% CI has broad intervals or/and includes both significant benefit of treatment and notable harm.</p>
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16 pages, 5022 KiB  
Article
The Role of the Mu Opioid Receptors of the Medial Prefrontal Cortex in the Modulation of Analgesia Induced by Acute Restraint Stress in Male Mice
by Yinan Du, Yukui Zhao, Aozhuo Zhang, Zhiwei Li, Chunling Wei, Qiaohua Zheng, Yanning Qiao, Yihui Liu, Wei Ren, Jing Han, Zongpeng Sun, Weiping Hu and Zhiqiang Liu
Int. J. Mol. Sci. 2024, 25(18), 9774; https://doi.org/10.3390/ijms25189774 - 10 Sep 2024
Viewed by 299
Abstract
Mu opioid receptors (MORs) represent a vital mechanism related to the modulation of stress-induced analgesia (SIA). Previous studies have reported on the gamma-aminobutyric acid (GABA)ergic “disinhibition” mechanisms of MORs on the descending pain modulatory pathway of SIA induced in the midbrain. However, the [...] Read more.
Mu opioid receptors (MORs) represent a vital mechanism related to the modulation of stress-induced analgesia (SIA). Previous studies have reported on the gamma-aminobutyric acid (GABA)ergic “disinhibition” mechanisms of MORs on the descending pain modulatory pathway of SIA induced in the midbrain. However, the role of the MORs expressed in the medial prefrontal cortex (mPFC), one of the main cortical areas participating in pain modulation, in SIA remains completely unknown. In this study, we investigated the contributions of MORs expressed on glutamatergic (MORGlut) and GABAergic (MORGABA) neurons of the medial prefrontal cortex (mPFC), as well as the functional role and activity of neurons projecting from the mPFC to the periaqueductal gray (PAG) region, in male mice. We achieved this through a combination of hot-plate tests, c-fos staining, and 1 h acute restraint stress exposure tests. The results showed that our acute restraint stress protocol produced mPFC MOR-dependent SIA effects. In particular, MORGABA was found to play a major role in modulating the effects of SIA, whereas MORGlut seemed to be unconnected to the process. We also found that mPFC–PAG projections were efficiently activated and played key roles in the effects of SIA, and their activation was mediated by MORGABA to a large extent. These results indicated that the activation of mPFC MORGABA due to restraint stress was able to activate mPFC–PAG projections in a potential “disinhibition” pathway that produced analgesic effects. These findings provide a potential theoretical basis for pain treatment or drug screening targeting the mPFC. Full article
(This article belongs to the Special Issue The Multiple Mechanisms Underlying Neuropathic Pain (III))
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Figure 1
<p>Acute restraint stress-induced analgesia (SIA). (<b>A</b>) Diagram of the stress and hot-plate test (HPT) procedures. (<b>B</b>) Effects of acute restraint stress on analgesia as assessed via an HPT; <span class="html-italic">n</span> = 9 for each group; ** <span class="html-italic">p</span> &lt; 0.01. (<b>C</b>) The percentage of the maximum possible effect (MPE%) from (<b>B</b>), calculated as MPE% = (post-test latency − pre-test latency)/(cut-off latency − pre-test latency) × 100%), the same below; the group data are shown as means ± SEMs; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The role of Mu opioid receptors (MORs) in the medial prefrontal cortex (mPFC) in SIA induced through acute restraint. (<b>A</b>) Flow diagram of the generation of MOR KO and MOR WT mice. (<b>B</b>) (<b>Left</b>): Schematic of in situ hybridization for <span class="html-italic">Oprm1</span> mRNA in the areas containing the mPFC in MOR KO and MOR WT mice. The <span class="html-italic">Oprm1</span> mRNA was stained in red, while the nucleus was stained in blue (DAPI). Scale bar = 500 µm. (<b>Right</b>): Higher-magnification images of the fields in the mPFC areas of MOR KO and MOR WT mice. Bar = 20 µm. (<b>C</b>) Quantitative analysis of the percentage of neurocyte MORs expressed in the mPFCs of MOR KO and MOR WT mice; <span class="html-italic">n</span> = 3 fields containing the median position of the right mPFC (acquired from different <span class="html-italic">n</span> = 3 animals) were used per group; MOR-positive cells and total neural cells were counted in each field; the form of data presentation was the MOR-positive cell/neural cell ratio. ** <span class="html-italic">p</span> &lt; 0.01. (<b>D</b>) Diagram of adeno-associated virus (rAAV) injection, stress, and HPT procedures. (<b>E</b>) Contributions of mPFC MORs to SIA induced through acute restraint as assessed via an HPT; <span class="html-italic">n</span> = 8 for each group; ** <span class="html-italic">p</span> &lt; 0.01. (<b>F</b>) Equated MPE% from the groups in (<b>E</b>), and data are shown as means ± SEMs; ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 3
<p>MORGlut in the mPFC plays a marginal part in the modulation of SIA induced by acute restraint. (<b>A</b>) Diagram of the generation of MORGlut cKO and MORGlut WT mice. (<b>B</b>) (<b>Left</b>): Schematic of in situ hybridization for <span class="html-italic">Oprm1</span> mRNA in the areas containing mPFC in MORGlut cKO and MORGlut WT mice. The <span class="html-italic">Oprm1</span> mRNA was stained in red, <span class="html-italic">vGlut1</span> mRNA was stained in green, and the nuclei were stained in blue (DAPI). Bar = 500 µm. (<b>Right</b>): Higher-magnification images of the fields in the mPFC areas in MORGlut cKO and MORGlut WT mice. The white arrowhead indicates a double-labeled cell with <span class="html-italic">Oprm1</span> mRNA and <span class="html-italic">vGlut1</span> mRNA, the yellow arrowheads represent <span class="html-italic">Oprm1</span> mRNA localization in <span class="html-italic">vGlut1</span>-negative cells, and the purple arrowheads represent <span class="html-italic">vGlut1</span>-positive cells without Oprm1 mRNA. Bar = 20 µm. (<b>C</b>) Quantitative analysis of the percentage of MORGlut expressed in the mPFCs of MORGlut KO and MORGlut WT mice; <span class="html-italic">n</span> = 3 fields containing the median position of the right mPFC (acquired from different <span class="html-italic">n</span> = 3 animals) were used per group; the double-positive cells and total Glut-positive cells were counted in each field; the form of data presentation was the double-positive cell/Glut-positive cell ratio; ** <span class="html-italic">p</span> &lt; 0.01. (<b>D</b>) Flow diagram of the rAAV injection, stress, and HPT procedures. (<b>E</b>) Rare contributions of mPFC MORGlut to SIA induced by acute restraint as assessed via an HPT; <span class="html-italic">n</span> = 7 for each group; ** <span class="html-italic">p</span> &lt; 0.01. (<b>F</b>) Equated MPE% from the groups in (<b>E</b>); data are shown as means ± SEMs.</p>
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<p>Contributions of mPFC MORGABA to SIA induced by acute restraint. (<b>A</b>) Diagram detailing the generation of MORGABA cKO and MORGABA WT mice. (<b>B</b>) (<b>Left</b>): Schematic of in situ hybridization for <span class="html-italic">Oprm1</span> mRNA in the areas containing mPFC in MORGABA cKO and MORGABA WT mice. The <span class="html-italic">Oprm1</span> mRNA was stained in red, <span class="html-italic">vGAT</span> mRNA was stained in green, and nuclei were stained in blue (DAPI). Bar = 500 µm. (<b>Right</b>): Higher-magnification images of the fields in the mPFC areas in MORGABA cKO and MORGABA WT mice. The white arrowhead indicates a double-labeled cell with <span class="html-italic">Oprm1</span> mRNA and <span class="html-italic">vGAT</span> mRNA, the yellow arrowheads represent <span class="html-italic">Oprm1</span> mRNA localization in <span class="html-italic">vGAT</span>-negative cells, and the purple arrowheads represent <span class="html-italic">vGAT</span>-positive cells without Oprm1 mRNA. Bar = 20 µm. (<b>C</b>) Quantitative analysis of the percentage of MORGABA expressed in the mPFCs of MORGABA KO and MORGABA WT mice; <span class="html-italic">n</span> = 3 fields containing the median position of the right mPFC (acquired from different <span class="html-italic">n</span> = 3 animals) were used per group; the double-positive cells and total GAT-positive were counted in each field; the form of data presentation was the double-positive cell/GAT-positive cell ratio; ** <span class="html-italic">p</span> &lt; 0.01. (<b>D</b>) Flow diagram of the rAAV injection, stress, and HPT procedures. (<b>E</b>) Contributions of mPFC MORGABA to SIA induced by acute restraint as assessed via an HPT; <span class="html-italic">n</span> = 8 for each group; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01. (<b>F</b>) Equated MPE% from the groups in (<b>E</b>), and the data are shown as means ± SEMs; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>mPFC–periaqueductal gray (PAG) projections are significant in the process of inducing SIA through acute restraint. (<b>A</b>) Diagram showing the labeling of mPFC–PAG projections. (<b>B</b>) Typical morphology of labeled mPFC–PAG projections. Bar = 200 µm. (<b>C</b>) (<b>Left</b>): Representative images showing c-fos expression in mPFC–PAG projections in our stressed and unstressed groups of mice. Bar = 100 µm. (<b>Right</b>): Higher-magnification images of the fields from the left. The white arrowhead indicates a double-labeled cell with labeled mPFC–PAG projections and c-fos. Bar = 20 µm. (<b>D</b>) Quantitative analysis of the percentage of c-fos positive-labeled mPFC–PAG projections from the mPFCs of mice in the stressed and unstressed groups; <span class="html-italic">n</span> = 3 fields containing the median position of the right mPFC (acquired from different <span class="html-italic">n</span> = 3 animals) were used per group; the c-fos-positive EYFP cells and total EYFP cells were counted in each field; the form of data presentation was the c-fos-positive EYFP cell/EYFP cell ratio. ** <span class="html-italic">p</span> &lt; 0.01. (<b>E</b>) Diagram detailing the mounting of an inhibitory chemogenetical module in mPFC–PAG projections and a flow diagram of rAAV injection, cannula insertion, CNO injection, stress, and HPT procedures. (<b>F</b>) The influence of the chemogenetic inhibition of mPFC–PAG projections on SIA induced by acute restraint as assessed via an HPT; <span class="html-italic">n</span> = 6 for each group; ** <span class="html-italic">p</span> &lt; 0.01. (<b>G</b>) Equated MPE% from the groups in (<b>F</b>), and data are shown as means ± SEM; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>MORGABA modulated the activity of mPFC–PAG projections during MOR-dependent SIA. (<b>A</b>) Diagram detailing the labeling of mPFC–PAG projections in MORGABA cKO and MORGABA WT mice. (<b>B</b>) Representative higher-magnification images showing c-fos expression in mPFC–PAG projections for MORGABA cKO (<b>B<sub>1</sub></b>) and MORGABA WT (<b>B<sub>2</sub></b>) mice under acute restraint stress or unstressed conditions. Bar = 20 µm; <span class="html-italic">n</span> = 3 fields containing the median position of the right mPFC (acquired from different <span class="html-italic">n</span> = 3 animals) were used per group; The white arrowheads represent co-labeling of EYFP and c-fos, the c-fos-positive EYFP cells and total EYFP cells were counted in each field; the form of data presentation was the c-fos-positive EYFP cell/EYFP cell ratio; ** <span class="html-italic">p</span> &lt; 0.01. (<b>C</b>) Quantitative analysis of the percentage of c-fos(+)-labeled mPFC–PAG projections in the mPFCs of MORGABA cKO (<b>C<sub>1</sub></b>) or MORGABA WT (<b>C<sub>2</sub></b>) mice under acute restraint stress or unstressed conditions; 3 fields were quantified from 3 animals; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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13 pages, 2487 KiB  
Article
The Effect of a Caffeine and Nicotine Combination on Nicotine Withdrawal Syndrome in Mice
by Zhe Chen, Naiyan Lu, Xu Li, Qingrun Liu, Yujie Li, Xiyue Li, Ximiao Yu, Haotian Zhao, Chang Liu, Xue Tang, Xun Wang and Weisun Huang
Nutrients 2024, 16(18), 3048; https://doi.org/10.3390/nu16183048 - 10 Sep 2024
Viewed by 481
Abstract
Nicotine dependence is an important cause of excessive exposure to tobacco combustion compounds in most smokers. Nicotine replacement therapy is the main method to treat nicotine dependence, but it still has its shortcomings, such as the inability to mitigate withdrawal effects and limited [...] Read more.
Nicotine dependence is an important cause of excessive exposure to tobacco combustion compounds in most smokers. Nicotine replacement therapy is the main method to treat nicotine dependence, but it still has its shortcomings, such as the inability to mitigate withdrawal effects and limited applicability. It has been hypothesized that a combination of low-dose nicotine and caffeine could achieve the same psychological stimulation effect as a high dose of nicotine without causing nicotine withdrawal effects. To establish a model of nicotine dependence, male C57BL/6J mice were subcutaneously injected four times a day with nicotine (2 mg/kg) for 15 days and fed with water containing nicotine at the same time. They were randomly divided into four groups. After 24 h of withdrawal, different groups were injected with saline, nicotine (0.25 mg/kg or 0.1 mg/kg), or nicotine (0.1 mg/kg) and caffeine (20 mg/kg). Behavioral and physiological changes were evaluated by an assessment of physical signs, open field tests, elevated plus maze experiments, forced swimming tests, hot plate tests, and new-object-recognition tests. The changes in dopamine release in the prefrontal cortex (PFC) and ventral tegmental area (VTA) in the midbrain were analyzed using ELISA. The results showed that a combination of caffeine and nicotine could effectively relieve nicotine withdrawal syndrome, increase movement ability and pain thresholds, reduce anxiety and depression, enhance memory and cognitive ability, and increase the level of dopamine release in the PFC and VTA. Thus, caffeine combined with nicotine has potential as a stable and effective treatment option to help humans with smoking cessation. Full article
(This article belongs to the Section Nutritional Epidemiology)
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Figure 1
<p>Effect of caffeine combined with nicotine on the physical signs and behavior of mice with nicotine withdrawal. (<b>A</b>) Physical sign score (F<sub>(3, 20)</sub> = 9.665, <span class="html-italic">p</span> &lt; 0.001). (<b>B</b>) Diagram of OFT. (<b>C</b>) Total movement distance (F<sub>(3, 20)</sub> = 40.70, <span class="html-italic">p</span> &lt; 0.0001). (<b>D</b>) Rest time (F<sub>(3, 20)</sub> = 41.17, <span class="html-italic">p</span> &lt; 0.0001). Movement track of mice in the last 5 min of OFT for different groups: (<b>E</b>) MOD, (<b>F</b>) H-NIC, (<b>G</b>) L-NIC, and (<b>H</b>) L-NIC+CAF. (<b>I</b>) Number of entries in center areas (F<sub>(3, 20)</sub> = 8.539, <span class="html-italic">p</span> &lt; 0.001). (<b>J</b>) Time spent in center and corner areas (F<sub>(3, 20)</sub> = 8.615, <span class="html-italic">p</span> &lt; 0.001; F<sub>(3, 20)</sub> = 8.735, <span class="html-italic">p</span> &lt; 0.001). MOD: Mice that received physiological saline. H-NIC: Mice that received nicotine solution at 0.25 mg/kg. L-NIC: Mice that received nicotine solution at 0.1 mg/kg. L-NIC+CAF: Mice that received mixture of nicotine and caffeine at 0.1 and 20 mg/kg, respectively. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001; ns: no significant difference.</p>
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<p>Effect of caffeine combined with nicotine on anxiety behavior and depressive behavior of mice with nicotine withdrawal. (<b>A</b>) Diagram of EPM (left: front view, right: top view). (<b>B</b>) Proportion of times they entered the open arms (PEO, F<sub>(3, 20)</sub> = 13.12, <span class="html-italic">p</span> &lt; 0.0001). (<b>C</b>) Proportion of time they dwelled in open arms (PDO, F<sub>(3, 20)</sub> = 10.76, <span class="html-italic">p</span> &lt; 0.001). (<b>D</b>) Diagram of FST. (<b>E</b>) Immobility time in FST (F<sub>(3, 20)</sub> = 15.80, <span class="html-italic">p</span> &lt; 0.0001). MOD: Mice that received physiological saline. H-NIC: Mice that received nicotine solution at 0.25 mg/kg. L-NIC: Mice that received nicotine solution at 0.1 mg/kg. L-NIC+CAF: Mice that received mixture of nicotine and caffeine at 0.1 and 20 mg/kg, respectively. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001; ns: no significant difference.</p>
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<p>Effect of caffeine combined with nicotine on pain latency of mice with nicotine withdrawal. (<b>A</b>) Diagram of HPT. (<b>B</b>) Pain latency in HPT (F<sub>(3, 20)</sub> = 9.306, <span class="html-italic">p</span> &lt; 0.001). MOD: Mice that received physiological saline. H-NIC: Mice that received nicotine solution at 0.25 mg/kg. L-NIC: Mice that received nicotine solution at 0.1 mg/kg. L-NIC+CAF: Mice that received mixture of nicotine and caffeine at 0.1 and 20 mg/kg, respectively. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01; ns: no significant difference.</p>
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<p>Effect of caffeine combined with nicotine on cognitive behavior of mice with nicotine withdrawal. (<b>A</b>) Diagram of NOR. (<b>B</b>) Exploration time of mice in NOR (D-value: difference value. F<sub>(3, 11.93)</sub> = 0.7628, <span class="html-italic">p</span> = 0.5365; F<sub>(3, 20)</sub> = 10.96, <span class="html-italic">p</span> &lt; 0.001; F<sub>(3, 20)</sub> = 11.05, <span class="html-italic">p</span> &lt; 0.001). MOD: Mice that received physiological saline. H-NIC: Mice that received nicotine solution at 0.25 mg/kg. L-NIC: Mice that received nicotine solution at 0.1 mg/kg. L-NIC+CAF: Mice that received mixture of nicotine and caffeine at 0.1 and 20 mg/kg, respectively. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01; ns: no significant difference.</p>
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<p>Effect of caffeine combined with nicotine on dopamine levels in the brain of withdrawal mice. (<b>A</b>) PFC (F<sub>(3, 20)</sub> = 46.78, <span class="html-italic">p</span> &lt; 0.0001). (<b>B</b>) VTA (F<sub>(3, 20)</sub> = 23.82, <span class="html-italic">p</span> &lt; 0.0001). MOD: Mice that received physiological saline. H-NIC: Mice that received nicotine solution at 0.25 mg/kg. L-NIC: Mice that received nicotine solution at 0.1 mg/kg. L-NIC+CAF: Mice that received mixture of nicotine and caffeine at 0.1 and 20 mg/kg, respectively. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001; ns: no significant difference.</p>
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18 pages, 2167 KiB  
Article
Antidepressant Effects of Ginsenoside Rc on L-Alpha-Aminoadipic Acid-Induced Astrocytic Ablation and Neuroinflammation in Mice
by Dohyung Kwon, Yunna Kim and Seung-Hun Cho
Int. J. Mol. Sci. 2024, 25(17), 9673; https://doi.org/10.3390/ijms25179673 - 6 Sep 2024
Viewed by 320
Abstract
Depression is a prevalent and debilitating mental disorder that affects millions worldwide. Current treatments, such as antidepressants targeting the serotonergic system, have limitations, including delayed onset of action and high rates of treatment resistance, necessitating novel therapeutic strategies. Ginsenoside Rc (G-Rc) has shown [...] Read more.
Depression is a prevalent and debilitating mental disorder that affects millions worldwide. Current treatments, such as antidepressants targeting the serotonergic system, have limitations, including delayed onset of action and high rates of treatment resistance, necessitating novel therapeutic strategies. Ginsenoside Rc (G-Rc) has shown potential anti-inflammatory and neuroprotective effects, but its antidepressant properties remain unexplored. This study investigated the antidepressant effects of G-Rc in an L-alpha-aminoadipic acid (L-AAA)-induced mouse model of depression, which mimics the astrocytic pathology and neuroinflammation observed in major depressive disorder. Mice were administered G-Rc, vehicle, or imipramine orally after L-AAA injection into the prefrontal cortex. G-Rc significantly reduced the immobility time in forced swimming and tail suspension tests compared to vehicle treatment, with more pronounced effects than imipramine. It also attenuated the expression of pro-inflammatory cytokines (TNF-α, IL-6, TGF-β, lipocalin-2) and alleviated astrocytic degeneration, as indicated by increased GFAP and decreased IBA-1 levels. Additionally, G-Rc modulated apoptosis-related proteins, decreasing caspase-3 and increasing Bcl-2 levels compared to the L-AAA-treated group. These findings suggest that G-Rc exerts antidepressant effects by regulating neuroinflammation, astrocyte–microglia crosstalk, and apoptotic pathways in the prefrontal cortex, highlighting its potential as a novel therapeutic agent for depression. Full article
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Graphical abstract
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<p>Assessment of locomotor activity and depression-like behavior: (<b>A</b>) Locomotion of mice was evaluated using an open-field test. The effects of G-Rc on depressive behavior were assessed using the (<b>B</b>) tail suspension test and (<b>C</b>) forced swimming test. Immobility time in both tests implies depression-like behavior. Results are expressed as the mean ± SEM; N = 5 per group; ## <span class="html-italic">p</span> &lt; 0.01 and ### <span class="html-italic">p</span> &lt; 0.001, significantly different from the sham control group (sham); *** <span class="html-italic">p</span> &lt; 0.001, significantly different from the vehicle-treated group (L-AAA + Veh); † <span class="html-italic">p</span> &lt; 0.05 and †† <span class="html-italic">p</span> &lt; 0.01, significantly different from the IMI-treated group (L-AAA + IMI). G-Rc, ginsenoside Rc; L-AAA, L-alpha-aminoadipic acid; TST, tail suspension test; FST, forced swimming test; SEM, standard error of the mean; IMI, imipramine.</p>
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<p>The anti-inflammatory effects of G-Rc were assessed using real-time PCR and ELISA: (<b>A</b>) Real-time PCR results for TNF-α, (<b>B</b>) IL-6, (<b>C</b>) TGF-β, and (<b>D</b>) LCN2 are presented. (<b>E</b>) ELISA results for LCN2 are also shown. Results are expressed as the mean ± SEM; N = 3 per group; ### <span class="html-italic">p</span> &lt; 0.001, significantly different from the sham control group (sham); ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001, significantly different from the vehicle-treated group (L-AAA + Veh); †† <span class="html-italic">p</span> &lt; 0.01 and ††† <span class="html-italic">p</span> &lt; 0.001, significantly different from L-AAA + IMI. G-Rc, ginsenoside Rc; L-AAA, L-alpha-aminoadipic acid; SEM, standard error of the mean; ELISA, enzyme-linked immunosorbent assay; TGF-β, transforming growth factor-beta; TNF-α, tumor necrosis factor-α; IL-6, interleukin-6; LCN2, lipocalin-2; IMI, imipramine.</p>
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<p>Effects of G-Rc on astrocyte and microglial markers following L-AAA administration: (<b>A</b>) The suppressive effects of G-Rc on L-AAA administration were evaluated via immunofluorescence staining. Representative results from immunohistochemistry are presented showing GFAP (red), IBA-1 (green), and DAPI (blue). After L-AAA injection, (<b>B</b>,<b>C</b>) the number and the area of GFAP-positive astrocytes (red) decreased, whereas (<b>E</b>,<b>F</b>) the number and the area of IBA-1-positive microglial cells (green) increased. However, the histopathological deficits were reduced in the G-Rc-treated (L-AAA + Rc) and IMI-treated (L-AAA + IMI) groups. Western blot analyses of (<b>D</b>) GFAP and (<b>G</b>) IBA-1 protein levels, respectively, were performed, and the results were consistent with the immunofluorescence findings, confirming the effects of L-AAA and G-Rc treatment on GFAP and IBA-1 expression. Scale bars are set at 100 μm. Quantitative data are expressed as the mean ± SEM; N = 3 per group; # <span class="html-italic">p</span> &lt; 0.05, ## <span class="html-italic">p</span> &lt; 0.01, ### <span class="html-italic">p</span> &lt; 0.001, significantly different from the sham control group (sham); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, significantly different from the vehicle-treated group (L-AAA + Veh); G-Rc, ginsenoside Rc; L-AAA, L-alpha-aminoadipic acid; GFAP, glial fibrillary acidic protein; IBA-1, ionized calcium-binding adaptor molecule 1; IMI, imipramine.</p>
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<p>Suppressive effects of G-Rc on apoptosis-related proteins following L-AAA administration: Western blot analysis of (<b>A</b>) caspase-3 protein levels and (<b>B</b>) Bcl-2 protein levels. Quantitative data are expressed as the mean ± SEM; N = 3 per group; # <span class="html-italic">p</span> &lt; 0.05, significantly different from the sham control group (sham); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, significantly different from the vehicle-treated group (L-AAA + Veh). G-Rc, ginsenoside Rc; L-AAA, L-alpha-aminoadipic acid; IMI, imipramine.</p>
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<p>(<b>A</b>) Timeline of the experiment, structural formula of (<b>B</b>) ginsenoside Rc and (<b>C</b>) imipramine, and (<b>D</b>) location of the L-AAA infusion. For injection of the prefrontal cortex, the following coordinates were used: AP 1.7 mm, ML ± 0.3 mm, and DV −2.5 mm from the bregma. Allen Mouse Brain Atlas, <a href="http://mouse.brain-map.org" target="_blank">mouse.brain-map.org</a> [<a href="#B108-ijms-25-09673" class="html-bibr">108</a>] (accessed on 8 July 2024) and <a href="http://atlas.brain-map.org" target="_blank">atlas.brain-map.org</a> [<a href="#B109-ijms-25-09673" class="html-bibr">109</a>] (accessed on 8 July 2024). L-AAA, L-alpha-aminoadipic acid; OFT, open-field test; FST, forced swimming test; TST, tail suspension test; AP, anterior–posterior; ML, medial–lateral; DV, dorsal–ventral.</p>
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16 pages, 1125 KiB  
Review
Monoaminergic Modulation of Learning and Cognitive Function in the Prefrontal Cortex
by Natalie Boyle, Sarah Betts and Hui Lu
Brain Sci. 2024, 14(9), 902; https://doi.org/10.3390/brainsci14090902 - 6 Sep 2024
Viewed by 504
Abstract
Extensive research has shed light on the cellular and functional underpinnings of higher cognition as influenced by the prefrontal cortex. Neurotransmitters act as key regulatory molecules within the PFC to assist with synchronizing cognitive state and arousal levels. The monoamine family of neurotransmitters, [...] Read more.
Extensive research has shed light on the cellular and functional underpinnings of higher cognition as influenced by the prefrontal cortex. Neurotransmitters act as key regulatory molecules within the PFC to assist with synchronizing cognitive state and arousal levels. The monoamine family of neurotransmitters, including dopamine, serotonin, and norepinephrine, play multifaceted roles in the cognitive processes behind learning and memory. The present review explores the organization and signaling patterns of monoamines within the PFC, as well as elucidates the numerous roles played by monoamines in learning and higher cognitive function. Full article
(This article belongs to the Section Molecular and Cellular Neuroscience)
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<p>Organization of monoamine projections to the prefrontal cortex. Key centers of monoamine production that project to the PFC in both humans (<b>top</b>) and rodents (<b>bottom</b>). Dopamine (red) includes the ventral tegmental area (VTA). Norepinephrine (green) includes the locus coeruleus (LC). Serotonin (blue) includes the dorsal raphe nucleus (DR) and median raphe nucleus (MnR). The MnR projection to the PFC is secondary to the DR, indicated by a dotted arrow.</p>
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<p>Dopamine and the reward prediction error (RPE). Studies have shown when a cue is first presented (light) followed by a reward stimulus (food), DA phasic burst response occurs at presentation of the reward. Following reinforcement learning, DA phasic burst activity shifts to the time of the cue presentation (light), encoding the reward prediction error.</p>
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10 pages, 640 KiB  
Article
Does the Transcranial Direct Current Stimulation Selectively Modulate Prefrontal Cortex Hemodynamics? An Immediate Effect-Controlled Trial on People with and without Depression
by Laura Oliveira Campos, Maria de Cassia Gomes Souza Macedo, Vheyda Katheleen Vespasiano Monerat, Kariny Realino do Rosário Ferreira, Mayra Evelise Cunha dos Santos, Arthur Ferreira Esquirio, Ana Luiza Guimarães Alves, Gabriela Lopes Gama, Michelle Almeida Barbosa and Alexandre Carvalho Barbosa
Appl. Sci. 2024, 14(17), 7901; https://doi.org/10.3390/app14177901 - 5 Sep 2024
Viewed by 339
Abstract
Despite the recommendation to treat depression using transcranial direct current stimulation (tDCS), novel findings raise doubts over the tDCS’s efficacy in managing depressive episodes. Neurophysiologic approaches to understanding the specificities of brain responses to tDCS in patients with depression remain to be explored. [...] Read more.
Despite the recommendation to treat depression using transcranial direct current stimulation (tDCS), novel findings raise doubts over the tDCS’s efficacy in managing depressive episodes. Neurophysiologic approaches to understanding the specificities of brain responses to tDCS in patients with depression remain to be explored. Objective: Our aim was to compare immediate hemodynamic responses to tDCS on the left dorsolateral prefrontal cortex (DLPFC; F3-Fp2 montage) in patients with depressive disorder and in controls (no additional stimuli). Methods: Sixteen participants were allocated to the depression group and sixteen to the control group. Both groups received 2 mA tDCS for 20 min, using the F3-Fp2 montage. The hemodynamic effect over the DLPFC was assessed using functional near-infrared intracranial spectroscopy (fNIRS) positioned on the left supraorbital region (Fp1). Mean, minimal, and maximal values of baseline and post-stimulation rates of oxygen saturation (SatO2) were recorded. The oxygenated hemoglobin rates (HbO) were extracted. Results: Between-group differences were detected for minimal baseline rates of SatO2 and HbO levels. The depression group showed lower results compared to the control group at baseline. After the protocol, only the depression group showed increased minimal rates of SatO2 and HbO. The post-tDCS minimal rates were equal for both groups. Conclusions: The findings showed immediate anodal tDCS effects over DLPFC hemodynamics. The effects were exclusive to the lowest baseline rate group and did not affect the normal oxygen rate group. The minimal increase in SatO2 and HbO rates after the protocol in the depression group suggests that those with reduced cerebral perfusion may be more affected by tDCS. Full article
(This article belongs to the Special Issue New Insights into Neurorehabilitation)
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<p>Flowchart of participant enrollment, allocation, and analysis in accordance with CONSORT guidelines. In the depression group, 16 participants were analyzed, with 4 participants excluded due to data loss caused by a collection error. All 16 participants in the control group were analyzed.</p>
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<p>Oxygen saturation (SatO<sub>2</sub>) group trends with regression equations. Legend: DG = depression group; CG = control group.</p>
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17 pages, 3351 KiB  
Article
Beneficial Effect of Dimethyl Fumarate Drug Repositioning in a Mouse Model of TDP-43-Dependent Frontotemporal Dementia
by Ignacio Silva-Llanes, Raquel Martín-Baquero, Alicia Berrojo-Armisen, Carmen Rodríguez-Cueto, Javier Fernández-Ruiz, Eva De Lago and Isabel Lastres-Becker
Antioxidants 2024, 13(9), 1072; https://doi.org/10.3390/antiox13091072 - 2 Sep 2024
Viewed by 428
Abstract
Frontotemporal dementia (FTD) causes progressive neurodegeneration in the frontal and temporal lobes, leading to behavioral, cognitive, and language impairments. With no effective treatment available, exploring new therapeutic approaches is critical. Recent research highlights the transcription factor Nuclear Factor erythroid-derived 2-like 2 (NRF2) as [...] Read more.
Frontotemporal dementia (FTD) causes progressive neurodegeneration in the frontal and temporal lobes, leading to behavioral, cognitive, and language impairments. With no effective treatment available, exploring new therapeutic approaches is critical. Recent research highlights the transcription factor Nuclear Factor erythroid-derived 2-like 2 (NRF2) as vital in limiting neurodegeneration, with its activation shown to mitigate FTD-related processes like inflammation. Dimethyl fumarate (DMF), an NRF2 activator, has demonstrated neuroprotective effects in a TAU-dependent FTD mouse model, reducing neurodegeneration and inflammation. This suggests DMF repositioning potential for FTD treatment. Until now, no trial had been conducted to analyze the effect of DMF on TDP-43-dependent FTD. In this study, we aimed to determine the potential therapeutic efficacy of DMF in a TDP-43-related FTD mouse model that exhibits early cognitive impairment. Mice received oral DMF treatment every other day from presymptomatic to symptomatic stages. By post-natal day (PND) 60, an improvement in cognitive function is already evident, becoming even more pronounced by PND90. This cognitive enhancement correlates with the neuroprotection observed in the dentate gyrus and a reduction in astrogliosis in the stratum lacunosum-moleculare zone. At the prefrontal cortex (PFC) level, a neuroprotective effect of DMF is also observed, accompanied by a reduction in astrogliosis. Collectively, our results suggest a potential therapeutic application of DMF for patients with TDP-43-dependent FTD. Full article
(This article belongs to the Special Issue Role of NRF2 Pathway in Neurodegenerative Diseases)
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<p>Response in the Novel Object Recognition (NOR) test of CAMKII-TDP-43 and WT mice at PND60 and PND90 after a chronic i.g. administration of DMF (100 mg/kg) from PND45 up to PND90 (<b>A</b>) Timeline representation of the experimental design: At PND45, we started the treatments (VEH, or DMF 100 mg/kg, i.g., respectively). At PND60, we performed the first NOR analysis (four consecutive days), and at PND90, before sacrifice, we performed another NOR test. (<b>B</b>,<b>C</b>) Analysis of the exploration time of familiar object (TF) and novel object (TN) at PND60 and PND90. (<b>D</b>,<b>E</b>) Analysis of the discrimination index at PND60 and PND90. (<b>F</b>,<b>G</b>) Analysis of the recognition index at PND60 and PND90. Bars indicate the mean of n = 10–12 samples ± SEM. Asterisks show significant differences with * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.005, **** <span class="html-italic">p</span> &lt; 0.001 comparing each group according to a one-way ANOVA followed by Tukey’s post-test.</p>
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<p>DMF has a neuroprotective effect on the granular cell layer of the dentate gyrus. (<b>A</b>) Immunofluorescence staining of DAPI and CALBINDIN-D28K of 30 μm-thick sections of the dentate gyrus of the hippocampus from WT, and CAMKII-TDP-43 mice treated with vehicle or DMF. (<b>B</b>) Quantification of the area stained with DAPI in the dentate gyrus from WT, and CAMKII-TDP-43 mice treated with vehicle or DMF. (<b>C</b>) Quantification of the intensity of the area stained with CALBINDIN-D28K in the dentate gyrus from WT, and CAMKII-TDP-43 mice treated with vehicle or DMF. (<b>D</b>) Quantification of the area stained with CALBINDIN-D28K in the dentate gyrus from WT, and CAMKII-TDP-43 mice treated with vehicle or DMF. Bars indicate the mean of n = 4–5 samples ± SEM. Asterisks show significant differences with * <span class="html-italic">p</span> &lt; 0.05 comparing each group according to a one-way ANOVA followed by Tukey’s post-test.</p>
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<p>DMF treatment modulates the astrogliosis observed in CAMKII-TDP-43 mice at the hippocampus. (<b>A</b>) Immunofluorescence of IBA1 (red) and GFAP (green), microglial and astrocytic markers, respectively, of 30 μm-thick sections in the CA1-hippocampus of mice treated with VEH or DMF, n = 4–5 samples ± SEM. Quantification of number of microglial (<b>B</b>) and astrocytes (<b>C</b>) cells at the CA1 area of mice treated with VEH or DMF, n = 4–5 samples ± SEM. RT-qPCR determination of mRNA levels of <span class="html-italic">Trem2</span> (<b>D</b>), <span class="html-italic">Il1b</span> (<b>E</b>), <span class="html-italic">Glast1</span> (<b>F</b>), and <span class="html-italic">Sphk2</span> (<b>G</b>) genes at the hippocampus of mice treated with VEH or DMF, n = 4–5 samples ± SEM. The asterisks represent the difference in significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, comparing each group according to a one-way ANOVA followed by Tukey’s post-test.</p>
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<p>TDP-43 overexpression decreases CTIP2+ neurons and DMF treatment partially reverses this effect in layer V of the cortex. (<b>A</b>) Immunofluorescence of CTIP2 (marker of corticospinal motor neurons and other projection neurons in layer V) (red) and DAPI (blue) of 30 μm-thick sections of the mPFC of mice treated with VEH or DMF, n = 5 samples ± SEM. (<b>B</b>) Quantification of number of CTIP2+ cells at the layer V of the mPFC of mice treated with VEH or DMF, n= 4–5 samples ± SEM. The asterisks represent the difference in significance: ** <span class="html-italic">p</span> &lt; 0.01, comparing each group according to a one-way ANOVA followed by Tukey’s post-test.</p>
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<p>The overexpression of TDP-43 induces astrogliosis in layer V of the mPFC and treatment with DMF partially reverses this effect. (<b>A</b>) Immunofluorescence of IBA1 (red) and S100B (green), microglial and astrocytic markers, respectively, of 30 μm-thick sections in the layer V of the mPFC of mice treated with VEH or DMF, n = 4–5 samples ± SEM. Quantification of number of microglial (<b>B</b>) and astrocyte (<b>C</b>) cells at the layer V of the mPFC of mice treated with VEH or DMF, n = 4–5 samples ± SEM. RT-qPCR determination of mRNA levels of <span class="html-italic">Il1b</span> (<b>D</b>) and <span class="html-italic">Sphk2</span> (<b>E</b>) genes in the same area, n = 4–5 samples ± SEM. The asterisks represent the difference in significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, comparing each group according to a one-way ANOVA followed by Tukey’s post-test.</p>
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<p>Hypothetical scheme of the neuroprotective effect of DMF treatment in a TDP-43-dependent FTD model. Our results suggest an NRF2-dependent effect mediated by the actions of DMF and MMF at the antioxidant level and an anti-inflammatory effect, which may be primarily mediated by DMF.</p>
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17 pages, 10016 KiB  
Article
Impacts of Electroconvulsive Therapy on the Neurometabolic Activity in a Mice Model of Depression: An Ex Vivo 1H-[13C]-NMR Spectroscopy Study
by Ajay Sarawagi, Pratishtha Wadnerkar, Vrundika Keluskar, Narra Sai Ram, Jerald Mahesh Kumar and Anant Bahadur Patel
Neuroglia 2024, 5(3), 306-322; https://doi.org/10.3390/neuroglia5030021 - 2 Sep 2024
Viewed by 283
Abstract
Electroconvulsive therapy (ECT) is an effective treatment for severe and drug-resistant depression, yet its mode of action remains poorly understood. This study aimed to evaluate the effects of ECT on neurometabolism using ex vivo 1H-[13C]-NMR spectroscopy in conjunction with intravenous [...] Read more.
Electroconvulsive therapy (ECT) is an effective treatment for severe and drug-resistant depression, yet its mode of action remains poorly understood. This study aimed to evaluate the effects of ECT on neurometabolism using ex vivo 1H-[13C]-NMR spectroscopy in conjunction with intravenous infusion of [1,6-13C2]glucose in a chronic variable mild stress (CVMS) model of depression. Both CVMS and control mice were subjected to seven sessions of electroconvulsive shock under mild isoflurane anesthesia. The CVMS mice exhibited a reduction in sucrose preference (CVMS 67.1 ± 14.9%, n = 5; CON 86.5 ± 0.6%, n = 5; p = 0.007), and an increase in immobility duration (175.9 ± 22.6 vs. 92.0 ± 23.0 s, p < 0.001) in the forced-swim test. The cerebral metabolic rates of glucose oxidation in glutamatergic (CMRGlc(Glu)) (CVMS 0.134 ± 0.015 µmol/g/min, n = 5; CON 0.201 ± 0.045 µmol/g/min, n = 5; padj = 0.04) and GABAergic neurons (CMRGlc(GABA)) (0.030 ± 0.002 vs. 0.046 ± 0.011 µmol/g/min, padj = 0.04) were reduced in the prefrontal cortex (PFC) of CVMS mice. ECT treatment in CVMS mice normalized sucrose preference [F(1,27) = 0.0024, p = 0.961] and immobility duration [F(1,28) = 0.434, p = 0.515], but not the time spent in the center zone (CVMS + ECT 10.4 ± 5.5 s, CON + sham 22.3 ± 11.4 s, padj = 0.0006) in the open field test. The ECT-treated CVMS mice exhibited reduced (padj = 0.021) CMRGlc(Glu) in PFC (0.169 ± 0.026 µmol/g/min, n = 8) when compared with CVMS mice, which underwent the sham procedure (0.226 ± 0.030 µmol/g/min, n = 8). These observations are consistent with ECT’s anticonvulsant hypothesis for its anti-depressive action. Full article
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<p>Experimental paradigm and behavioral measures in CVMS mice. (<b>a</b>) Timeline depicting different interventions and assessment of depression phenotype. (<b>b</b>) Sucrose preference, (<b>c</b>) Immobility duration in the forced-swim test (FST), and (<b>d</b>) Time spent in the open arms of the elevated-plus maze (EPM) test. The vertical bar represents the mean ± SD of the group, while the symbols depict individual values.</p>
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<p>Measurement of <sup>13</sup>C labeling of brain metabolites. Representative <sup>1</sup>H-[<sup>13</sup>C]-NMR spectra from prefrontal cortex (PFC) extracts of (<b>a</b>) Control, and (<b>b</b>) CVMS mice. Concentrations of <sup>13</sup>C-labeled metabolites in (<b>c</b>) PFC, and (<b>d</b>) Hippocampus of CVMS and control mice. The [1,6-<sup>13</sup>C<sub>2</sub>]glucose was administered in mice for 10 min, and <sup>1</sup>H-[<sup>13</sup>C]-NMR spectra were recorded in the brain tissue extracts. The spectra in the topmost panel <sup>1</sup>H-[<sup>12</sup>C+<sup>13</sup>C] represent the total concentration of neurometabolites, whereas the lower panel depicts <sup>13</sup>C-labeled neurometabolites. The concentrations of <sup>13</sup>C-labeled neurometabolites were measured in <sup>1</sup>H-[<sup>13</sup>C]-NMR spectra using [2-<sup>13</sup>C]glycine. Abbreviations: Ala<sub>C3</sub>, alanine-C3; Asp<sub>C3</sub>, aspartate-C3; Cre, creatine; GABA<sub>C2</sub>, γ-aminobutyric acid-C2; GABA<sub>C4</sub>, γ-aminobutyric acid-C4; Glu<sub>C4</sub>, glutamate-C4; Glu<sub>C3</sub>, glutamate-C3; Gln<sub>C4</sub>, glutamine-C4; Lac<sub>C3</sub>, lactate-C3; NAA, N-acetyl aspartate.</p>
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<p>Cerebral metabolic rates of glucose oxidation in the (<b>a</b>) Prefrontal cortex, and (<b>b</b>) Hippocampus of CVMS and control mice. The rates of glucose oxidation (<span class="html-italic">CMR<sub>Glc</sub></span><sub>(<span class="html-italic">Ox</span>)</sub>) were calculated using Equations (1)–(3). The vertical bar represents the mean ± SD of the group, while the symbols depict individual values.</p>
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<p>Assessment of ECT’s impact on depression-like phenotypes. (<b>a</b>) Experimental timeline, (<b>b</b>) Sucrose preference during CVMS paradigm, (<b>c</b>) Time spent in the immobile state in forced-swim test (FST), (<b>d</b>) Duration in the center plus open arms of the elevated plus maze (EPM) after CVMS. CVMS mice were subjected to a combination of variable stressors for 28 days and were given the choice of 2% sucrose solution and water throughout the experiment to measure the sucrose preference. EPM and FST were conducted after the completion of the CVMS paradigm. The impact of ECT on (<b>e</b>) Sucrose preference, (<b>f</b>) Immobility duration, and (<b>g</b>) Time spent in the center zone of OFT in CVMS and control mice. The ECT group of mice was given one electroconvulsive shock daily for seven consecutive days under mild isoflurane anesthesia, while the sham mice were subjected to isoflurane anesthesia but no electric shock. The unpaired two-tailed T-test was performed to assess the statistical significance of the difference for FST and EPM tests following CVMS. The statistical analysis for other measures was carried out using a Two-Way ANOVA along with Tukey’s method for multiple comparisons.</p>
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<p>Representative <sup>1</sup>H-[<sup>13</sup>C]-NMR spectra from PFC extract of (<b>a</b>) Control, and (<b>b</b>) CVMS mice. Mice were anesthetized using urethane and infused with [1,6-<sup>13</sup>C<sub>2</sub>]glucose using tail-vein for 10 min, and <sup>1</sup>H-[<sup>13</sup>C]-NMR spectra were recorded in brain tissue extracts at 600 MHz NMR spectrometer. The spectra in the uppermost panel (<sup>1</sup>H-[<sup>12</sup>C+<sup>13</sup>C]) represent the total concentration of neurometabolites, while those in the lower panel depict the level of <sup>13</sup>C-labeled neurometabolites. Abbreviations: Ala<sub>C3</sub>, alanine-C3; Asp<sub>C3</sub>, aspartate-C3; Cre, creatine; GABA<sub>C2</sub>, γ-aminobutyric acid-C2; GABA<sub>C4</sub>, γ-aminobutyric acid-C4; Glu<sub>C4</sub>, glutamate-C4; Glu<sub>C3</sub>, glutamate-C3; Gln<sub>C4</sub>, glutamine-C4; Lac<sub>C3</sub>, lactate-C3; NAA, N-acetyl aspartate.</p>
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<p>The impact of ECT on the rates of glucose oxidation (<span class="html-italic">CMR<sub>Glc</sub></span><sub>(<span class="html-italic">Ox</span>)</sub>) in (<b>a</b>) Prefrontal cortex and (<b>b</b>) Hippocampus. The <span class="html-italic">CMR<sub>Glc</sub></span><sub>(<span class="html-italic">Ox</span>)</sub> was calculated using Equations (1)–(3), based on the <sup>13</sup>C-label trapped into different neurometabolites. The vertical bar represents the mean ± SD of the group, while the symbols depict individual values.</p>
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25 pages, 19318 KiB  
Article
Spatiotemporal Dysregulation of Neuron–Glia Related Genes and Pro-/Anti-Inflammatory miRNAs in the 5xFAD Mouse Model of Alzheimer’s Disease
by Marta Ianni, Miriam Corraliza-Gomez, Tiago Costa-Coelho, Mafalda Ferreira-Manso, Sara Inteiro-Oliveira, Nuno Alemãn-Serrano, Ana M. Sebastião, Gonçalo Garcia, Maria José Diógenes and Dora Brites
Int. J. Mol. Sci. 2024, 25(17), 9475; https://doi.org/10.3390/ijms25179475 - 31 Aug 2024
Viewed by 686
Abstract
Alzheimer’s disease (AD), the leading cause of dementia, is a multifactorial disease influenced by aging, genetics, and environmental factors. miRNAs are crucial regulators of gene expression and play significant roles in AD onset and progression. This exploratory study analyzed the expression levels of [...] Read more.
Alzheimer’s disease (AD), the leading cause of dementia, is a multifactorial disease influenced by aging, genetics, and environmental factors. miRNAs are crucial regulators of gene expression and play significant roles in AD onset and progression. This exploratory study analyzed the expression levels of 28 genes and 5 miRNAs (miR-124-3p, miR-125b-5p, miR-21-5p, miR-146a-5p, and miR-155-5p) related to AD pathology and neuroimmune responses using RT-qPCR. Analyses were conducted in the prefrontal cortex (PFC) and the hippocampus (HPC) of the 5xFAD mouse AD model at 6 and 9 months old. Data highlighted upregulated genes encoding for glial fibrillary acidic protein (Gfap), triggering receptor expressed on myeloid cells (Trem2) and cystatin F (Cst7), in the 5xFAD mice at both regions and ages highlighting their roles as critical disease players and potential biomarkers. Overexpression of genes encoding for CCAAT enhancer-binding protein alpha (Cebpa) and myelin proteolipid protein (Plp) in the PFC, as well as for BCL2 apoptosis regulator (Bcl2) and purinergic receptor P2Y12 (P2yr12) in the HPC, together with upregulated microRNA(miR)-146a-5p in the PFC, prevailed in 9-month-old animals. miR-155 positively correlated with miR-146a and miR-21 in the PFC, and miR-125b positively correlated with miR-155, miR-21, while miR-146a in the HPC. Correlations between genes and miRNAs were dynamic, varying by genotype, region, and age, suggesting an intricate, disease-modulated interaction between miRNAs and target pathways. These findings contribute to our understanding of miRNAs as therapeutic targets for AD, given their multifaceted effects on neurons and glial cells. Full article
(This article belongs to the Special Issue Molecular Research on Neurodegenerative Diseases 4.0)
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<p>Heatmap displaying the major individual sources of variation in each of our pre-selected miRNAs and genes, by RT-qPCR analysis. Results were obtained by a three-way ANOVA (α = 0.05), with Šídák’s multiple comparisons test for the factors: age (6 vs. 9 months), genotype (<span class="html-italic">WT</span> vs. <span class="html-italic">5xFAD</span>), and region (hippocampus vs. prefrontal cortex). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Significant differences in gene expression profiles in the prefrontal cortex (PFC) and hippocampus (HPC) of <span class="html-italic">5xFAD</span> mice, at 6 and 9 months of age assessed by RT-qPCR analysis. Upregulation of <span class="html-italic">Gfap</span> (<b>A</b>), <span class="html-italic">Trem2</span> (<b>B</b>), and <span class="html-italic">Cst7</span> (<b>C</b>) was revealed to be the most consistent markers, as they are also not dependent on age or brain region. <span class="html-italic">Cebpa</span> (<b>D</b>) and <span class="html-italic">Plp</span> (<b>E</b>) were shown to increase with aging in the PFC of <span class="html-italic">5xFAD</span> mice. <span class="html-italic">Traf6</span> (<b>F</b>) showed regional-dependent differences only in control mice, whereas the <span class="html-italic">Bcl2</span> (<b>G</b>) and P2yr12 (<b>H</b>) genes exhibited elevated levels in the HPC compared to the PFC. Three-way ANOVA (α = 0.05) with Šídák’s multiple comparisons test for the following factors: age (6 vs. 9 months), genotype (<span class="html-italic">WT</span> vs. <span class="html-italic">5xFAD</span>), and region (HPC vs. PFC). Statistical significance: Age comparisons: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001; genotype comparisons <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 and <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001; region comparisons: <sup>§</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>§§</sup> <span class="html-italic">p</span> &lt; 0.01 and <sup>§§§</sup> <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Inflamma-miRNA network and gene ontology. (<b>A</b>) miRNA–target network-map for the analyzed microRNAs and targets selected for this study. (<b>B</b>) miR-124-3p gene ontology enriched analysis. (<b>C</b>) miR-155-5p gene ontology enriched analysis. (<b>D</b>) miR-125b-5p gene ontology enriched analysis. (<b>E</b>) miR-21-5p gene ontology enriched analysis. (<b>F</b>) miR-146a-3p gene ontology enriched analysis.</p>
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<p>Inflamma-miRNA expression levels, heat map diagram, and cluster analysis of miRNAs and genes considering age and genotype. (<b>A</b>) Expression level of each hit inflamma-miRNA by RT-qPCR. Three-way ANOVA (α = 0.05), with Šídák’s multiple comparisons test for the following factors: age (6 vs. 9 months), genotype (<span class="html-italic">WT</span> vs. <span class="html-italic">5xFAD</span>), and region (hippocampus vs. prefrontal cortex). Age comparisons: <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) Clustered heatmap of the differentially expressed genes and miRNAs. The parameters whose expression is greater in the case group are shown in green and those smaller in red. Darker colors represent less significant values.</p>
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<p>Correlation analysis between the investigated set of miRNAs and gene expression levels in the <span class="html-italic">5xFAD</span> mouse model of AD. The correlation matrix represents the pairwise correlations using Pearson’s correlation coefficient. Positive correlations are shown in blue and negative correlations in red. Color intensity and size of the circles are proportional to the correlation coefficient. (<b>A</b>) <span class="html-italic">WT</span>, 6 months hippocampus; (<b>B</b>) <span class="html-italic">5xFAD</span>, 6 months hippocampus; (<b>C</b>) <span class="html-italic">WT</span>, 9 months hippocampus; (<b>D</b>) <span class="html-italic">5xFAD</span>, 9 months hippocampus; (<b>E</b>) <span class="html-italic">WT</span>, 6 months prefrontal cortex; (<b>F</b>) <span class="html-italic">5xFAD</span>, 6 months prefrontal cortex; (<b>G</b>) <span class="html-italic">WT</span>, 9 months prefrontal cortex; (<b>H</b>) <span class="html-italic">5xFAD</span>, 9 months prefrontal cortex. Significance of Pearson’s correlation coefficients: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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13 pages, 2537 KiB  
Article
Gender Differences in Prefrontal Cortex Response to Negative Emotional Stimuli in Drivers
by Ferran Balada, Anton Aluja, Óscar García, Neus Aymamí and Luis F. García
Brain Sci. 2024, 14(9), 884; https://doi.org/10.3390/brainsci14090884 - 30 Aug 2024
Viewed by 372
Abstract
Background: Road safety improvement is a governmental priority due to driver-caused accidents. Driving style variation affects safety, with emotional regulation being pivotal. However, functional near-infrared spectroscopy (fNIRS) studies show inconsistent prefrontal cortex activity during emotion processing. This study examines prefrontal cortex response to [...] Read more.
Background: Road safety improvement is a governmental priority due to driver-caused accidents. Driving style variation affects safety, with emotional regulation being pivotal. However, functional near-infrared spectroscopy (fNIRS) studies show inconsistent prefrontal cortex activity during emotion processing. This study examines prefrontal cortex response to negative emotional stimuli, particularly traffic accident images, across drivers diverse in age and gender. Method: The study involved 118 healthy males (44.38 ± 12.98 years) and 84 females (38.89 ± 10.60 years). The Multidimensional Driving Style Inventory (MDSI) was used to assess driving behavior alongside fNIRS recordings. Participants viewed traffic accident and neutral images while prefrontal oxygenation was monitored. Results: Women rated traffic accidents (t-test = 2.43; p < 0.016) and neutral images (t-test = 2.19; p < 0.030) lower in valence than men. Arousal differences were significant for traffic accident images (t-test = −3.06; p < 0.002). correlational analysis found an inverse relationship between Dissociative scale scores and oxygenation (all p-values ≤ 0.013). Greater prefrontal oxygenation occurred with neutral images compared to traffic accidents. Left hemisphere differences (t-test = 3.23; p < 0.001) exceeded right hemisphere differences (t-test = 2.46; p < 0.015). Subgroup analysis showed male participants to be driving these disparities. Among adaptive drivers, significant oxygenation differences between neutral and accident images were evident in both hemispheres (left: t-test = 2.72, p < 0.009; right: t-test = 2.22, p < 0.030). Conclusions: Male drivers with maladaptive driving styles, particularly dissociative ones, exhibit reduced prefrontal oxygenation when exposed to neutral and traffic accident images. This response was absent in female drivers, with no notable age-related differences. Full article
(This article belongs to the Section Neuropsychology)
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<p>(<b>A</b>) Sensor used for fNIR recording with indication of the channels. (<b>B</b>) Example of images of each of the four blocks presented. On the left are the neutral valence images. On the right are images related to traffic accidents. (<b>C</b>) Screenshot of the fNIR signal-recording software used to obtain the data. The lower part of the image shows the register corresponding to each of the 16 channels analysed.</p>
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<p>Relationships between Dissociative scale scores and oxygenation level changes (μmol/L) in each quadrant of prefrontal cortex.</p>
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<p>Differences in oxygenation of the prefrontal cortex viewing neutral and traffic accident pictures in men and women (* <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.005).</p>
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<p>Differences in oxygenation of the prefrontal cortex viewing neutral and traffic accident pictures in men and women (* <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.005).</p>
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<p>Differences in oxygenation of the prefrontal cortex viewing neutral and traffic accident pictures in low, medium, and high maladaptive style groups measured by the MDSI.</p>
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12 pages, 773 KiB  
Article
On Metacognition: Overconfidence in Word Recall Prediction and Its Association with Psychotic Symptoms in Patients with Schizophrenia
by Yvonne Flores-Medina, Regina Ávila Bretherton, Jesús Ramírez-Bermudez, Ricardo Saracco-Alvarez and Monica Flores-Ramos
Brain Sci. 2024, 14(9), 872; https://doi.org/10.3390/brainsci14090872 - 29 Aug 2024
Viewed by 753
Abstract
A two-factor account has been proposed as an explanatory model for the formation and maintenance of delusions. The first factor refers to a neurocognitive process leading to a significant change in subjective experience; the second factor has been regarded as a failure in [...] Read more.
A two-factor account has been proposed as an explanatory model for the formation and maintenance of delusions. The first factor refers to a neurocognitive process leading to a significant change in subjective experience; the second factor has been regarded as a failure in hypothesis evaluation characterized by an impairment in metacognitive ability. This study was focused on the assessment of metacognition in patients with schizophrenia. The aims of the study were to measure the overconfidence in metacognitive judgments through the prediction of word list recall and to analyze the correlation between basic neurocognition (memory and executive function) and metacognition through a metamemory test and the severity of psychotic symptoms. Method: Fifty-one participants with a diagnosis of schizophrenia were evaluated. The Positive and Negative Syndrome Scale (PANSS) was used to assess the severity of psychiatric symptoms, and the subtest of metamemory included in the Executive Functions and Frontal Lobe-2 battery (BANFE-2) was used to evaluate overconfidence and underestimation errors, intrusion and perseverative response, total volume of recall, and Brief Functioning Assessment Scale (FAST) for social functioning. Results: The strongest correlation is observed between overconfidence errors and the positive factor of the PANSS (r = 0.774, p < 0.001). For the enter model in the multiple linear regression (r = 0.78, r2 = 0.61; F = 24.57, p < 0.001), the only significant predictor was overconfidence errors. Conclusion: Our results highlight the relevance of a metacognitive bias of overconfidence, strongly correlated with psychotic symptoms, and support the hypothesis that metacognitive defects contribute to the failure to reject contradictory evidence. From our perspective, these findings align with current mechanistic models of schizophrenia that focus on the role of the prefrontal cortex. Full article
(This article belongs to the Special Issue Cognitive Dysfunction in Schizophrenia)
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<p>Heatmap of Pearson’s correlations between overconfidence errors, underestimation errors, total recall in memory, intrusion, and perseverative responses; and PANSS total score, as well as the five-factor scores and the FAST score. *** <span class="html-italic">p</span> &lt; 0.001. After Bonferroni’s correction.</p>
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<p>Regression Plots. The model was constructed using positive symptoms as dependent variable, and overconfidence errors, total or metacognitive errors, and intrusion responses as predictors. We observed a strong correlation between overestimation errors (panel <b>a</b>) and positive symptoms but not with the total or metacognitive errors (panel <b>b</b>) or the intrusion response (panel <b>c</b>).</p>
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57 pages, 557 KiB  
Review
Biomarkers of Internet Gaming Disorder—A Narrative Review
by Katarzyna Skok and Napoleon Waszkiewicz
J. Clin. Med. 2024, 13(17), 5110; https://doi.org/10.3390/jcm13175110 - 28 Aug 2024
Viewed by 417
Abstract
Since game mechanics and their visual aspects have become more and more addictive, there is concern about the growing prevalence of Internet gaming disorder (IGD). In the current narrative review, we searched PubMed and Google Scholar databases for the keywords “igd biomarker gaming” [...] Read more.
Since game mechanics and their visual aspects have become more and more addictive, there is concern about the growing prevalence of Internet gaming disorder (IGD). In the current narrative review, we searched PubMed and Google Scholar databases for the keywords “igd biomarker gaming” and terms related to biomarker modalities. The biomarkers we found are grouped into several categories based on a measurement method and are discussed in the light of theoretical addiction models (tripartite neurocognitive model, I-PACE). Both theories point to gaming-related problems with salience and inhibition. The first dysfunction makes an individual more susceptible to game stimuli (raised reward seeking), and the second negatively impacts resistance to these stimuli (decreased cognitive control). The IGD patients’ hypersensitivity to reward manifests mostly in ventral striatum (VS) measurements. However, there is also empirical support for a ventral-to-dorsal striatal shift and transition from goal-directed to habitual behaviors. The deficits in executive control are demonstrated in parameters related to the prefrontal cortex (PFC), especially the dorsolateral prefrontal cortex (DLPFC). In general, the connection of PFC with reward under cortex nuclei seems to be dysregulated. Other biomarkers include reduced P3 amplitudes, high-frequency heart rate variability (HRV), and the number of eye blinks and saccadic eye movements during the non-resting state. A few studies propose a diagnostic (multimodal) model of IGD. The current review also comments on inconsistencies in findings in the nucleus accumbens (NAcc), anterior cingulate cortex (ACC), and precuneus and makes suggestions for future IGD studies. Full article
(This article belongs to the Topic New Advances in Addiction Behavior)
34 pages, 376 KiB  
Review
EEG Techniques with Brain Activity Localization, Specifically LORETA, and Its Applicability in Monitoring Schizophrenia
by Angelina Zeltser, Aleksandra Ochneva, Daria Riabinina, Valeria Zakurazhnaya, Anna Tsurina, Elizaveta Golubeva, Alexander Berdalin, Denis Andreyuk, Elena Leonteva, Georgy Kostyuk and Anna Morozova
J. Clin. Med. 2024, 13(17), 5108; https://doi.org/10.3390/jcm13175108 - 28 Aug 2024
Viewed by 484
Abstract
Background/Objectives: Electroencephalography (EEG) is considered a standard but powerful tool for the diagnosis of neurological and psychiatric diseases. With modern imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and magnetoencephalography (MEG), source localization can be improved, especially with low-resolution [...] Read more.
Background/Objectives: Electroencephalography (EEG) is considered a standard but powerful tool for the diagnosis of neurological and psychiatric diseases. With modern imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and magnetoencephalography (MEG), source localization can be improved, especially with low-resolution brain electromagnetic tomography (LORETA). The aim of this review is to explore the variety of modern techniques with emphasis on the efficacy of LORETA in detecting brain activity patterns in schizophrenia. The study’s novelty lies in the comprehensive survey of EEG methods and detailed exploration of LORETA in schizophrenia research. This evaluation aligns with clinical objectives and has been performed for the first time. Methods: The study is split into two sections. Part I examines different EEG methodologies and adjuncts to detail brain activity in deep layers in articles published between 2018 and 2023 in PubMed. Part II focuses on the role of LORETA in investigating structural and functional changes in schizophrenia in studies published between 1999 and 2024 in PubMed. Results: Combining imaging techniques and EEG provides opportunities for mapping brain activity. Using LORETA, studies of schizophrenia have identified hemispheric asymmetry, especially increased activity in the left hemisphere. Cognitive deficits were associated with decreased activity in the dorsolateral prefrontal cortex and other areas. Comparison of the first episode of schizophrenia and a chronic one may help to classify structural change as a cause or as a consequence of the disorder. Antipsychotic drugs such as olanzapine or clozapine showed a change in P300 source density and increased activity in the delta and theta bands. Conclusions: Given the relatively low spatial resolution of LORETA, the method offers benefits such as accessibility, high temporal resolution, and the ability to map depth layers, emphasizing the potential of LORETA in monitoring the progression and treatment response in schizophrenia. Full article
(This article belongs to the Section Nuclear Medicine & Radiology)
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