(1) Background: Working memory, which involves temporary storage, information processing, and regulating attention resources, is a fundamental cognitive process and constitutes a significant component of neuroscience research. This study aimed to evaluate brain activation patterns by analyzing functional magnetic resonance imaging (fMRI) time-series data collected during a designed N-back working memory task with varying cognitive demands. (2) Methods: We utilized a novel transformer model, blood oxygen level-dependent transformer (BolT), to extract the activation level features of brain regions in the cognitive process, thereby obtaining the influence weights of regions of interest (ROIs) on the corresponding tasks. (3) Results: Compared with previous studies, our work reached similar conclusions in major brain region performance and provides a more precise analysis for identifying brain activation patterns. For each type of working memory task, we selected the top 5 percent of the most influential ROIs and conducted a comprehensive analysis and discussion. Additionally, we explored the effect of prior knowledge conditions on the performance of different tasks in the same period and the same tasks at different times. (4) Conclusions: The comparison results reflect the brain's adaptive strategies and dependencies in coping with different levels of cognitive demands and the stability optimization of the brain's cognitive processing. This study introduces innovative methodologies for understanding brain function and cognitive processes, highlighting the potential of transformer in cognitive neuroscience. Its findings offer new insights into brain activity patterns associated with working memory, contributing to the broader landscape of neuroscience research.

Objective markers of pathophysiological processes underlying lifetime depression and mania/hypomania risk can provide biologically informed targets for novel interventions to help prevent the onset of affective disorders in individuals with subsyndromal symptoms. Greater activity within and functional connectivity (FC) between the central executive network (CEN), supporting emotional regulation (ER) subcomponent processes such as working memory (WM), the default mode network (DMN), supporting self-related information processing, and the salience network (SN), is thought to interfere with cognitive functioning and predispose to depressive disorders. Using an emotional n-back paradigm designed to examine WM and ER capacity, we examined in young adults: (1) relationships among activity and FC in these networks and lifetime depression and mania/hypomania risk; (2) the extent to which these relationships were specific to lifetime depression risk versus lifetime mania/hypomania risk; (3) whether findings in a first, Discovery sample n = 101, 63 female, age = 23.85 (2.9) could be replicated in a two independent Test samples of young adults: Test sample 1: n = 90, 60 female, age = 21.7 (2.0); Test sample 2: n = 96, 65 female, age = 21.6 (2.1). The Mood Spectrum Self-Report (MOODS-SR-L) assessed lifetime mania/hypomania risk and depression risk. We showed significant clusters of activity to each contrast in similar locations in the anatomic mask in each Test sample as in the Discovery sample, and, using extracted mean BOLD signal from these clusters as IVs, we showed similar patterns of IV-DV relationships in each Test sample as in the Discovery sample. Specifically, in the Discovery sample, greater DMN activity during WM was associated with greater lifetime depression risk. This finding was specific to depression and replicated in both independent samples (all ps<0.05 qFDR). Greater CEN activity during ER was associated with increased lifetime depression risk and lifetime mania/hypomania risk in all three samples (all ps< 0.05 qFDR). These replicated findings provide promising objective, neural markers to better identify, and guide and monitor early interventions for, depression and mania/hypomania risk in young adults.

Deficits in working memory (WM) are common in patients with Major Depression Disorder (MDD). Previous research mainly in healthy adults indicated that physical exercise training may improve cognitive functions by stimulating neuronal plasticity particularly in hippocampal structures. Thus, the goal of this functional Magnetic Resonance Imaging (fMRI) study was to examine alterations in neuronal activity during a WM task and to investigate changes in brain volume and functioning following a physical exercise training in patients with MDD with a specific focus on hippocampal structures.

86 (39 female) MDD outpatients (average age 37.3), diagnosed by clinical psychologists, were randomly assigned to one of three groups for a 12-week intervention: High intensity exercise training (HEX), low intensity exercise training (LEX) or waiting list control group (WL). An n-back task (with WM loads of 0, 1, 2, and 3) during fMRI was conducted before and after interventions/waiting period.

Both exercise groups showed better performance and shorter reaction times at higher WM loads after 12-weeks of physical exercise training. Specifically in the HEX, we found an improvement in physical fitness and an increase in neural activation in the left hippocampus as compared to the WL following the exercise training. Training-related structural volume changes in gray matter or hippocampus were not detected.

Our results partly support the hypothesis that physical exercise training positively affects WM functions by improving neuronal plasticity in hippocampal regions. Exercise training seems to be a promising intervention to improve deficient WM performance in patients with MDD.

Neurobiological correlates and mechanisms of the augmentation of psychotherapy with endurance exercise in mild to moderate depression - SPeED, http://apps.who.int/trialsearch/Trial2.aspx?TrialID=DRKS00008869, DRKS00008869.

Mitochondrial complex I is the largest enzyme complex in the respiratory chain and can be non-invasively measured using [18F]BCPP-EF positron emission tomography (PET). Neurological conditions associated with mitochondria complex I pathology are also associated with altered blood oxygen level-dependent (BOLD) response and impairments in cognition. This study aims to investigate the relationship between mitochondrial complex I levels, cognitive function, and associated neural activity during task switching in healthy humans.

Cognitively healthy adults (n=23) underwent [18F]BCPP-EF PET scans and functional magnetic resonance imaging (fMRI) while performing a task-switching exercise. Task performance metrics included switch cost and switching accuracy. Data were analysed using linear mixed-effects models and partial least squares regression (PLS-R).

We found significant positive associations between [18F]BCPP-EF VT and the task-switching fMRI response (β=3.351, SE=1.01, z=3.249, p=0.001). Positive Pearson's correlations between [18F]BCPP-EF VT and the fMRI response were observed in the dorsolateral prefrontal cortex (r=0.61, p=0.0019), insula (r=0.46, p=0.0264) parietal-precuneus (r=0.51, p=0.0139) and anterior cingulate cortex (r=0.45, p=0.0293). [18F]BCPP-EF VT across task-relevant regions was associated with task switching accuracy (PLS-R, R2=0.48, RMSE=0.154, p=0.011) and with switch cost (PLS-R, R2=0.38, RMSE=0.07, p=0.048).

Higher mitochondrial complex I levels may underlie an individual's ability to exhibit a stronger BOLD response during task switching and are associated with better task-switching performance. This provides the first evidence linking the BOLD response with mitochondrial complex I and suggests a possible biological mechanism for aberrant BOLD response in conditions associated with mitochondrial complex I dysfunction that should be tested in future studies.

Task-based functional magnetic resonance imaging (tb-fMRI) has advanced our understanding of brain-behavior relationships. Standard tb-fMRI analyses suffer from limited reliability and low effect sizes, and machine learning (ML) approaches often require thousands of subjects, restricting their ability to inform how brain function may arise from and contribute to individual differences. Using data from 9,024 early adolescents, we derived a classifier ('neural signature') distinguishing between high and low working memory loads in an emotional n-back fMRI task, which captures individual differences in the separability of activation to the two task conditions. Signature predictions were more reliable and had stronger associations with task performance, cognition, and psychopathology than standard estimates of regional brain activation. Further, the signature was more sensitive to psychopathology associations and required a smaller training sample (N=320) than standard ML approaches. Neural signatures hold tremendous promise for enhancing the informativeness of tb-fMRI individual differences research and revitalizing its use.

Schizophrenia and attention deficit hyperactivity disorder (ADHD) have many contradicting features, but both these disorders share inattention as a core symptom. This study explored how the characteristics of inattention differ between the two disorders. 20 patients with schizophrenia, 20 patients with adult ADHD and 20 healthy controls participated in this study. Comprehensive attention test, Korean Wechsler adult intelligence scale-IV and resting-state functional magnetic resonance imaging (fMRI) were collected, among other things. The schizophrenia and ADHD groups showed low and high levels of functional connectivity in the default mode network (DMN), respectively. Functional connectivity level within the DMN was also positively correlated with processing speed index in the schizophrenia group and positively correlated with the number of divided-attention commission errors in the ADHD group. These results show that schizophrenia and adult ADHD have similarities in the characteristics of attention deficit, in that both may arise from dysregulation within the DMN. However, the differences in the levels of functional connectivity in the DMN between these groups affect how inattention manifests in each group.

Previous investigations have revealed performance deficits and altered neural processes during working-memory (WM) tasks in major depressive disorder (MDD). While most of these studies used task-based functional magnetic resonance imaging (fMRI), there is an increasing interest in resting-state fMRI to characterize aberrant network dynamics involved in this and other MDD-associated symptoms. It has been proposed that activity during the resting-state represents characteristics of brain-wide functional organization, which could be highly relevant for the efficient execution of cognitive tasks. However, the dynamics linking resting-state properties and task-evoked activity remain poorly understood. Therefore, the present study investigated the association between spontaneous activity as indicated by the amplitude of low frequency fluctuations (ALFF) at rest and activity during an emotional n-back task. 60 patients diagnosed with an acute MDD episode, and 52 healthy controls underwent the fMRI scanning procedure. Within both groups, positive correlations between spontaneous activity at rest and task-activation were found in core regions of the central-executive network (CEN), whereas spontaneous activity correlated negatively with task-deactivation in regions of the default mode network (DMN). Compared to healthy controls, patients showed a decreased rest-task correlation in the left prefrontal cortex (CEN) and an increased negative correlation in the precuneus/posterior cingulate cortex (DMN). Interestingly, no significant group-differences within those regions were found solely at rest or during the task. The results underpin the potential value and importance of resting-state markers for the understanding of dysfunctional network dynamics and neural substrates of cognitive processing.

The cerebellar vermis is implicated in cognition and emotion, two key components of the psychotic-affective spectrum that includes schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD).

Volumes [N = 391; 97 SCZ, 78 BD, 103 MDD, and 113 healthy controls (HC)] and seed-to-whole brain functional connectivity (FC) [N = 136; 33 SCZ, 23 BD, 51 MDD, and 29 HC] of total vermis and its subregions, V1 (anterior), V2 (posterior superior), and V3 (posterior inferior), were examined across SCZ, BD, MDD, and HC in samples enriched for first episode individuals. The relationship between vermis volumes and FC and cognitive measures were explored.

Significant diagnosis (p = 0.05) and diagnosis by subregion (p = 0.02) effects on vermis volumes were observed across the four groups, particularly in V2 (p = 0.01) with decreased V2 volumes in SCZ compared to BD (pFDR = 0.01). SCZ, BD, and MDD had significant effects on vermis FC, with SCZ having the greatest effects. SCZ had effects on FC of V1, V2, and V3 with broadly distributed cortical and subcortical regions, while BD and MDD effects were observed in FC of V2 and V3 with frontotemporal regions. Exploratory analyses found significant canonical correlation between V3 FC and WM and visual learning for SCZ and MDD. No significant associations were shown between vermis volumes and cognitive measures.

Structural and functional alterations of the vermis appear to vary across the psychotic-affective spectrum of SCZ, BD, and MDD. Posterior vermis may be a key neural intersection between affective and psychotic psychopathology.

Working memory impairment is a prominent feature of schizophrenia which predicts clinical and functional outcomes. Preclinical data suggest histamine-3 receptor (H3R) expression in cortical pyramidal neurons may have a role in working memory, and post-mortem data has found disruptions of H3R expression in schizophrenia.

We examined the role of H3R in vivo to elucidate its role on working memory impairment in schizophrenia.

We used positron emission tomography (PET) with the selective H3R radioligand [11C]MK-8278 to measure H3R availability, and employed a task during functional magnetic resonance imaging (fMRI) to assess working memory-evoked brain activation and cognitive task performance, in patients with schizophrenia (n = 12) and matched healthy volunteers (n = 12). We assessed the relationship between H3R availability and both task performance and working memory-evoked brain activation in regions of interest (ROIs), including the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC).

Patients with schizophrenia showed a strong positive correlation, after corrections for multiple comparisons, between ACC H3R availability and task performance (rho = 0.73, p = 0.007), which was absent in the control group (rho = 0.03, p = 0.94). Further ROI analysis did not find a significant relationship between H3R availability and working memory-evoked brain activation.

These results provide support for the role of H3R on working memory processes in patients with schizophrenia.

The hippocampus plays a critical role in learning and memory, and mice with epileptic cognitive impairment exhibit hippocampal atrophy. However, there is still a lack of research on the hippocampal cell atlas related to these disorders. Here, we utilized snRNA-seq to characterize the transcriptomic changes in hippocampal neurons of drug-resistant epilepsy (DRE) cognitive-impaired mice. The intercellular heterogeneity of 20 subpopulations of neurons was analyzed, focusing on aspects such as cell communication, gene expressions, GO and KEGG enrichment analysis, and module gene set analysis. Based on the degree of relevance to synaptic biological functions, the subpopulations associated with cognitive impairment (ExN1, 3, 8 and InN1, 6) were preliminarily identified. We also identified some key biomarkers in DRE cognitive-impaired mice, such as Ptprz1 and Calb1. Finally, we integrate and validate our dataset using identified well-annotated marker genes in the hippocampal region, further supporting the functional annotation of neuronal subpopulations.

Studies suggest severe mental disorders (SMDs), such as schizophrenia, major depressive disorder and bipolar disorder, are associated with common alterations in brain activity, albeit with a graded level of impairment. However, discrepancies between study findings likely to results from both small sample sizes and the use of different functional magnetic resonance imaging (fMRI) tasks. To address these issues, data-driven meta-analytic approach designed to identify homogeneous brain co-activity patterns across tasks was conducted to better characterize the common and distinct alterations between these disorders.

A hierarchical clustering analysis was conducted to identify groups of studies reporting similar neuroimaging results, independent of task type and psychiatric diagnosis. A traditional meta-analysis (activation likelihood estimation) was then performed within each of these groups of studies to extract their aberrant activation maps.

A total of 762 fMRI study contrasts were targeted, comprising 13 991 patients with SMDs. Hierarchical clustering analysis identified 5 groups of studies (meta-analytic groupings; MAGs) being characterized by distinct aberrant activation patterns across SMDs: (1) emotion processing; (2) cognitive processing; (3) motor processes, (4) reward processing, and (5) visual processing. While MAG1 was mostly commonly impaired, MAG2 was more impaired in schizophrenia, while MAG3 and MAG5 revealed no differences between disorder. MAG4 showed the strongest between-diagnoses differences, particularly in the striatum, posterior cingulate cortex, and ventromedial prefrontal cortex.

SMDs are characterized mostly by common deficits in brain networks, although differences between disorders are also present. This study highlights the importance of studying SMDs simultaneously rather than independently.

Brain entropy (BEN), which measures the amount of information in brain activity, provides a novel perspective for evaluating brain function. Recent studies using resting-state functional magnetic resonance imaging (fMRI) have shown that BEN during rest can help characterize brain function alterations in schizophrenia (SCZ). However, there is a lack of research on BEN using task-evoked fMRI to explore task-dependent cognitive deficits in SCZ. In this study, we evaluate whether the reduced working memory (WM) capacity in SCZ is possibly associated with dynamic changes in task BEN during tasks with high cognitive demands. We analyzed data from 15 patients with SCZ and 15 healthy controls (HC), calculating task BEN from their N-back task fMRI scans. We then examined correlations between task BEN values, clinical symptoms, 2-back task performance, and neuropsychological test scores. Patients with SCZ exhibited significantly reduced task BEN in the cerebellum, hippocampus, parahippocampal gyrus, thalamus, and the middle and superior frontal gyrus (MFG and SFG) compared to HC. In HC, significant positive correlations were observed between task BEN and 2-back accuracy in several brain regions, including the MFG and SFG; such correlations were absent in patients with SCZ. Additionally, task BEN was negatively associated with scores for both positive and negative symptoms in areas including the parahippocampal gyrus among patients with SCZ. In conclusion, our findings indicate that a reduction in BEN within prefrontal and hippocampal regions during cognitively demanding tasks may serve as a neuroimaging marker for SCZ.

Chronic ketamine use leads to cognitive impairments, however, the neural mechanisms underpinning these impairments are still unclear.

Many studies showed Anterior cingulate cortex (ACC)is strongly involved in cognition and drug addiction, as supported by our previous studies. The objective of this study was to assess the variations in resting-state functional connectivity (FC) changes in the right anterior cingulate cortex (ACC) of chronic ketamine users (CKUs) and their relationship with cognitive performance.

The study enrolled 28 chronic ketamine users (CKUs) and 30 healthy controls (HCs). Resting-state functional magnetic resonance imaging (fMRI) data were gathered from both groups. Cognitive functions were evaluated using the MATRICS Consensus Cognitive Battery (MCCB).

CKUs demonstrated significantly poorer cognitive performance than HCs in various cognitive domains, including Visual Learning, Speed of Processing, Working Memory, and the composite score of MCCB. Group-level comparisons revealed that CKUs exhibited enhanced functional connectivity between the right ACC and the right postcentral gyrus (PCG) compared to HCs. There was a positive relationship between the connectivity of right ACC-PCG and reasoning and problem-solving score, but there was no significant association with the characteristics of ketamine use.

CKUs showed enhanced connectivity between the right ACC and the right PCG. This enhanced functional connectivity may indicate functional compensation for cognitive deficits in CKUs, especially for reasoning and problem-solving impairments in CKUs.

Selective serotonin reuptake inhibitors (SSRIs) are among the most important antidepressants. However, there is limited research on predicting the occurrence of treatment-resistant depression (TRD) after 5 years. Examining the predictive effect of TRD occurrence using resting-state fMRI in patients initiating SSRIs treatment at the onset of major depressive disorder (MDD) could potentially enhance TRD management.

A total of 60 first-episode drug-naive MDD patients who met the criteria, along with 41 healthy controls of Han Chinese ethnicity, were recruited. All MDD patients received SSRIs as the initial treatment for relieving depressive symptoms. Resting-state fMRI scans were conducted for all subjects. Follow-up assessments were conducted over a period of five years, during which MDD patients were categorized into treatment-resistant depression (TRD) and non-treatment-resistant depression (NRD) groups based on disease progression. Amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), and Regional Homogeneity (ReHo) values were calculated and compared among the three groups. Additionally, receiver operating characteristic (ROC) curves were employed to identify potential predictors.

After 5 years of follow-up, it was found that 43 MDD patients were classified as NRD, while 17 were classified as TRD. In comparison to TRD, NRD exhibited decreased ALFF in the left middle cingulum gyrus (MCG.L) and in the right middle frontal gyrus (MFG.R), as well as decreased ReHo in MCG.L. Furthermore, NRD showed increased fALFF in the left precuneus (PCUN.L). The area under the curve (AUC) values were as follows: 0.724 (MCG.L by ALFF), 0.732 (MFG.R), 0.767 (PCUN.L), 0.774 (MCG.L by ReHo), 0.878 (combined), 0.547 (HAMD), and 0.408 (HAMA) respectively.

The findings suggest that PCUN.L, MFG.R, MCG.L, and the combined measures may indicate the possibility of developing TRD after 5 years when SSRIs are used as the initial therapy for relieving depressive symptoms in MDD patients.

Working memory (WM) is a distributed and dynamic process, and WM deficits are recognized as one of the top-ranked endophenotype candidates for major depressive disorders (MDD). However, there is a lack of knowledge of brain temporal-spatial profile of WM deficits in MDD. We used the dynamical degree centrality (dDC) to investigate the whole-brain temporal-spatial profile in 40 MDD and 40 controls during an n-back task with 2 conditions (i.e., '0back' and '2back'). We explored the dDC temporal variability and clustered meta-stable states in 2 groups during different WM conditions. Pearson's correlation analysis was used to evaluate the relationship between the altered dynamics with clinical symptoms and WM performance. Compared with controls, under '2back vs. 0back' contrast, patients showed an elevated dDC variability in wide range of brain regions, including the middle frontal gyrus, orbital part of inferior frontal gyrus (IFGorb), hippocampus, and middle temporal gyrus. Furthermore, the increased dDC variability in the hippocampus and IFGorb correlated with worse WM performance. However, there were no significant group-related differences in the meta-stable states were observed. This study demonstrated the increased WM-related instability (i.e., the elevated dDC variability) was represented in MDD, and enhancing stability may help patients achieve better WM performance.

Adolescents are high-risk population for major depressive disorder. Executive dysfunction emerges as a common feature of depression and exerts a significant influence on the social functionality of adolescents. This study aimed to identify the multimodal co-varying brain network related to executive function in adolescent with major depressive disorder. A total of 24 adolescent major depressive disorder patients and 43 healthy controls were included and completed the Intra-Extra Dimensional Set Shift Task. Multimodal neuroimaging data, including the amplitude of low-frequency fluctuations from resting-state functional magnetic resonance imaging and gray matter volume from structural magnetic resonance imaging, were combined with executive function using a supervised fusion method named multimodal canonical correlation analysis with reference plus joint independent component analysis. The major depressive disorder showed more total errors than the healthy controls in the Intra-Extra Dimensional Set Shift task. Their performance on the Intra-Extra Dimensional Set Shift Task was negatively related to the 14-item Hamilton Rating Scale for Anxiety score. We discovered an executive function-related multimodal fronto-occipito-temporal network with lower amplitude of low-frequency fluctuation and gray matter volume loadings in major depressive disorder. The gray matter component of the identified network was negatively related to errors made in Intra-Extra Dimensional Set Shift while positively related to stages completed. These findings may help to deepen our understanding of the pathophysiological mechanisms of cognitive dysfunction in adolescent depression.

The dynamics of large-scale networks, which are known as distributed sets of functionally synchronized brain regions and include the visual network (VIN), somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network (FPN), and default mode network (DMN), play important roles in emotional and cognitive processes in humans. Although disruptions in these large-scale networks are considered critical for the pathophysiological mechanisms of psychiatric disorders, their role in psychiatric disorders remains unknown. We aimed to elucidate the aberrant dynamics across large-scale networks in patients with schizophrenia (SZ) and mood disorders.

We performed energy-landscape analysis to investigate the aberrant brain dynamics of seven large-scale networks across 50 healthy controls (HCs), 36 patients with SZ, and 42 patients with major depressive disorder (MDD) recruited at Wakayama Medical University. We identified major patterns of brain activity using energy-landscape analysis and estimated their duration, occurrence, and ease of transition.

We identified four major brain activity patterns that were characterized by the activation patterns of the DMN and VIN (state 1, DMN (-) VIN (-); state 2, DMN (+) VIN (+); state 3, DMN (-) VIN (+); and state 4, DMN (+) VIN (-)). The duration of state 1 and the occurrence of states 1 and 2 were shorter in the SZ group than in HCs and the MDD group, and the duration of state 3 was longer in the SZ group. The ease of transition between states 3 and 4 was larger in the SZ group than in the HCs and the MDD group. The ease of transition from state 3 to state 4 was negatively associated with verbal fluency in patients with SZ. The current study showed that the brain dynamics was more disrupted in SZ than in MDD.

Energy-landscape analysis revealed aberrant brain dynamics across large-scale networks between SZ and MDD and their associations with cognitive abilities in SZ, which cannot be captured by conventional functional connectivity analyses. These results provide new insights into the pathophysiological mechanisms underlying SZ and mood disorders.

Mood and psychotic disorders are both associated with verbal memory impairments. Verbal memory represents an important treatment target for both disorders. However, whether the neurocognitive and neurophysiological profiles of verbal memory impairments differ between specific disorders within these two diagnostic categories and healthy controls remains unclear. The current systematic review synthesized findings from comparative studies which used behavioural and neuroimaging tasks to investigate verbal memory impairments between: (1) mood disorder, psychotic disorder, and healthy control groups; and (2) mood disorder without psychotic features, mood disorder with psychotic features, and healthy control groups.

The search strategy combined terms related to three main concepts: 'mood disorders', 'psychotic disorders', and 'verbal memory'. Searches were executed in Embase, MEDLINE, PsycInfo, and PubMed databases. A total of 38 articles met the full eligibility criteria and were included in the final narrative synthesis. Findings were stratified by memory domain (overall composite score, verbal working memory, immediate recall, delayed recall, and recognition memory) and by illness phase (acute and non-acute).

Mood and psychotic disorders displayed consistent verbal memory impairments compared to healthy controls during the acute and non-acute phases. Few significant differences were identified in the literature between mood and psychotic disorders, and between mood disorders with and without psychotic features. Individuals with schizophrenia were found to have decreased immediate and delayed verbal recall performance compared to bipolar disorder groups during the acute phase. Major depressive disorder groups with psychotic features were also found to have decreased delayed verbal recall performance compared to those without psychosis during the acute phase. No consistent differences were identified between mood and psychotic disorders during the non-acute phase. Finally, preliminary evidence suggests there may be functional abnormalities in important frontal and temporal brain regions related to verbal memory difficulties in both mood and psychotic disorders.

The current findings have potential implications for the diagnosis and treatment of cognitive impairments in mood and psychotic disorders. Verbal recall memory may serve as a sensitive tool in the risk stratification of cognitive impairments for certain mood and psychotic disorders. Moreover, since no widespread differences between clinical groups were identified, the evidence supports providing targeted interventions for verbal memory, such as pharmacological and non-pharmacological interventions, through a trans-diagnostic approach in mood and psychotic disorders.

A critical unanswered question about therapeutic transcranial magnetic stimulation is what patients should do during treatment to optimize its effectiveness. Here, we address this lack of knowledge in healthy participants, testing the hypotheses that stimulating the left dorsolateral prefrontal cortex (dlPFC) while participants perform a working memory task will provide stronger effects on subsequent activation, perfusion, connectivity, and performance than stimulating resting dlPFC.

After a baseline functional magnetic resonance imaging session to localize dlPFC activation and the associated frontoparietal network (FPN) engaged by an n-back task, healthy participants (N = 40, 67.5% female) underwent 3 counterbalanced sessions, separated by several weeks, during which they received intermittent theta burst stimulation (iTBS) followed by magnetic resonance imaging scans as follows: 1) iTBS to the dlPFC while resting passively (passive), 2) iTBS to the dlPFC while performing the n-back task (active), and 3) iTBS to a vertex site, while not engaged in the n-back task and resting passively (control).

We found no difference in n-back performance between the 3 conditions. However, FPN activation was reduced while performing the n-back task in the active condition relative to the passive and control conditions. There was no differential activity in the FPN on comparing passive with control conditions, i.e., there was no effect of the site of stimulation. We found no effects of state or site of stimulation on perfusion or connectivity with the dlPFC.

In this study, the state of the brain while receiving iTBS affected FPN activation, possibly reflecting greater efficiency of FPN network activation when participants were stimulated while engaging the FPN.

The pathophysiology of schizophrenia and bipolar disorder involves a complex interaction between genetic and environmental factors that begins in the early stages of neurodevelopment. Recent advancements in the field of induced pluripotent stem cells (iPSCs) offer a promising tool for understanding the neurobiological alterations involved in these disorders and, potentially, for developing new treatment options. In this review, we summarize the results of iPSC-based research on schizophrenia and bipolar disorder, showing disturbances in neurodevelopmental processes, imbalance in glutamatergic-GABAergic transmission and neuromorphological alterations. The limitations of the reviewed literature are also highlighted, particularly the methodological heterogeneity of the studies, the limited number of studies developing iPSC models of both diseases simultaneously, and the lack of in-depth clinical characterization of the included samples. Further studies are needed to advance knowledge on the common and disease-specific pathophysiological features of schizophrenia and bipolar disorder and to promote the development of new treatment options.

Although studies have confirmed that working memory (WM) is impaired among adults with major depressive disorder (MDD), generalizing these neurocognitive impairments to adolescents with MDD would be tenuous. Therefore, separate studies for adolescents with MDD are needed. Relatively little is known about the neural processes associated with WM dysfunction in adolescents with MDD. Thus, we examined whether adolescents with MDD have abnormal brain activation patterns compared to healthy controls (HC) during WM tasks and whether it was possible to distinguish adolescents with MDD and HC based on mean oxy-hemoglobin (Oxy-Hb) changes.

A total of 87 adolescents with MDD and 63 HC were recruited. Functional near-infrared spectroscopy (fNIRS) was performed to monitor the concentrations of Oxy-Hb in the frontotemporal lobe while participants performed three WM tasks in order to examine WM impairments in adolescents with depression.

The mean changes in Oxy-Hb concentrations in the left prefrontal cortex and right prefrontal cortex were higher among HC than among patients during the encoding and maintenance phase under each WM-load task. Machine learning was used to distinguish adolescents with MDD and HC based on Oxy-Hb changes, with a moderate area under the curve of 0.84.

This study revealed WM defects in adolescents with MDD compared to HC based on mean Oxy-Hb changes, which can be valuable for distinguishing adolescents with MDD from HC.

Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive, affective and behavioral tasks, adapted for the functional magnetic resonance imaging (MRI) (fMRI) experimental environment. There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders.

To investigate whether there exist specific neural circuits which underpin differential item responses to depressive, paranoid and neutral items (DN) in patients respectively with schizophrenia (SCZ) and major depressive disorder (MDD).

60 patients were recruited with SCZ and MDD. All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm, comprised of block design, including blocks with items from diagnostic paranoid (DP), depression specific (DS) and DN from general interest scale. We performed a two-sample t-test between the two groups-SCZ patients and depressive patients. Our purpose was to observe different brain networks which were activated during a specific condition of the task, respectively DS, DP, DN.

Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task. We identified one component that is task-related and independent of condition (shared between all three conditions), composed by regions within the temporal (right superior and middle temporal gyri), frontal (left middle and inferior frontal gyri) and limbic/salience system (right anterior insula). Another component is related to both diagnostic specific conditions (DS and DP) e.g. It is shared between DEP and SCZ, and includes frontal motor/language and parietal areas. One specific component is modulated preferentially by to the DP condition, and is related mainly to prefrontal regions, whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus, several occipital areas, including lingual and fusiform gyrus, as well as parahippocampal gyrus. Finally, component 12 appeared to be unique for the neutral condition. In addition, there have been determined circuits across components, which are either common, or distinct in the preferential processing of the sub-scales of the task.

This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.

Neuropsychiatric disorder (ND) is often accompanied by abnormal functional connectivity (FC) patterns in specific task contexts. The distinctive task-specific FC patterns can provide valuable features for ND classification models using deep learning. However, most previous studies rely solely on the whole-brain FC matrix without considering the prior knowledge of task-specific FC patterns. Insight by the decoding studies on brain-behavior relationship, we develop TSP-GNN, which extracts task-specific prior (TSP) connectome patterns and employs graph neural network (GNN) for disease classification. TSP-GNN was validated using publicly available datasets. Our results demonstrate that different ND types show distinct task-specific connectivity patterns. Compared with the whole-brain node characteristics, utilizing task-specific nodes enhances the accuracy of ND classification. TSP-GNN comprises the first attempt to incorporate prior task-specific connectome patterns and the power of deep learning. This study elucidates the association between brain dysfunction and specific cognitive processes, offering valuable insights into the cognitive mechanism of neuropsychiatric disease.

Very early-onset schizophrenia (VEOS) is a form of schizophrenia that manifests before the age of 13 years and is characterized by the presence of positive, negative, and disorganized symptoms. The condition is exceptionally rare and, to date, limited studies have been conducted, resulting in incomplete information about its clinical features.

The present study involves a systematic review of the existing literature regarding the clinical features and comorbidities of VEOS.

The first search retrieved 384 studies. Of these, 366 were removed following the application of exclusion criteria, resulting in 18 studies for the final set.

The results highlight that VEOS shares similarities with early-onset and adult-onset schizophrenia but also exhibits distinct and recognizable characteristics, including a more severe clinical profile (particularly in females), increased visual hallucinations, and high comorbidities with neurodevelopmental disorders. These findings may support clinicians in formulating early diagnoses and developing effective treatment strategies for pediatric and adolescent patients with psychosis.

Neuroimaging-based connectome studies have indicated that major depressive disorder (MDD) is associated with disrupted topological organization of large-scale brain networks. However, the disruptions and their clinical and cognitive relevance are not well established for morphological brain networks in adolescent MDD.

To investigate the topological alterations of single-subject morphological brain networks in adolescent MDD.

Twenty-five first-episode, treatment-naive adolescents with MDD and 19 healthy controls (HCs) underwent T1-weighted magnetic resonance imaging and a battery of neuropsychological tests. Single-subject morphological brain networks were constructed separately based on cortical thickness, fractal dimension, gyrification index, and sulcus depth, and topologically characterized by graph-based approaches. Between-group differences were inferred by permutation testing. For significant alterations, partial correlations were used to examine their associations with clinical and neuropsychological variables in the patients. Finally, a support vector machine was used to classify the patients from controls.

Compared with the HCs, the patients exhibited topological alterations only in cortical thickness-based networks characterized by higher nodal centralities in parietal (left primary sensory cortex) but lower nodal centralities in temporal (left parabelt complex, right perirhinal ectorhinal cortex, right area PHT and right ventral visual complex) regions. Moreover, decreased nodal centralities of some temporal regions were correlated with cognitive dysfunction and clinical characteristics of the patients. These results were largely reproducible for binary and weighted network analyses. Finally, topological properties of the cortical thickness-based networks were able to distinguish the MDD adolescents from HCs with 87.6% accuracy.

Adolescent MDD is associated with disrupted topological organization of morphological brain networks, and the disruptions provide potential biomarkers for diagnosing and monitoring the disease.

Whether remitted major depressive disorder (rMDD) and MDD present common or distinct neuropathological mechanisms remains unclear. We performed a meta-analysis of task-related whole-brain functional magnetic resonance imaging (fMRI) using anisotropic effect-size signed differential mapping software to compare brain activation between rMDD/MDD patients and healthy controls (HCs). We included 18 rMDD studies (458 patients and 476 HCs) and 120 MDD studies (3746 patients and 3863 HCs). The results showed that MDD and rMDD patients shared increased neural activation in the right temporal pole and right superior temporal gyrus. Several brain regions, including the right middle temporal gyrus, left inferior parietal, prefrontal cortex, left superior frontal gyrus and striatum, differed significantly between MDD and rMDD. Meta-regression analyses revealed that the percentage of females with MDD was positively associated with brain activity in the right lenticular nucleus/putamen. Our results provide valuable insights into the underlying neuropathology of brain dysfunction in MDD, developing more targeted and efficacious treatment and intervention strategies, and more importantly, providing potential neuroimaging targets for the early screening of MDD.

Aluminum (Al) is an important environmental pathogenic factor for neurodegenerative diseases, especially mild cognitive impairment (MCI). The aim of this study was to evaluate the gray matter volume of structural covariance network alterations in patients with Al-induced MCI. Male subjects who had been exposed to Al for >10 years were included in the present study. The plasma Al concentration, Montreal cognitive assessment (MoCA) score, and verbal memory assessed by the Rey auditory verbal learning test (AVLT) score were collected from each participant. Nonnegative matrix factorization was used to identify the structural covariance network. The neural structural basis for patients with Al-induced MCI was investigated using correlation analysis and group comparison. Plasma Al concentration was inversely related to MoCA scores, particularly AVLT scores. In patients with Al-induced MCI, the gray matter volume of the default mode network (DMN) was considerably lower than that in controls. Positive correlations were discovered between the DMN and MoCA scores as well as between the DMN and AVLT scores. In sum, long-term occupational Al exposure has a negative impact on cognition, primarily by affecting delayed recognition. The reduced gray matter volume of the DMN may be the neural mechanism of Al-induced MCI.

Deficits in cognition like working memory (WM) are highly prevalent symptoms related to major depressive disorder (MDD). Neuroimaging studies have described frontoparietal abnormalities in patients with MDD as a basis for these deficits. Based on research in healthy adults, it is hypothesized that increased physical fitness might be a protective factor for these deficits in MDD. However, the relationship between physical fitness and WM-related neural activity and performance has not been tested in MDD, to date. Understanding these associations could inform the development of physical exercise interventions in MDD.

Within a larger project, 111 (53female) MDD outpatients and 56 (34female) healthy controls performed an n-back task (0-, 1-, 2-, 3-back) during functional Magnetic Resonance Imaging. Physical fitness from a graded exercise test on a cycle ergometer was performed by 106 MDD patients.

Patients showed reduced performance particularly at high loads of the n-back WM task and prolonged reaction times at all n-back loads. A whole-brain interaction analysis of group by WM load revealed reduced neural activity in six frontoparietal clusters at medium and high WM loads in MDD patients compared to healthy controls. Analysis of covariance within the MDD sample showed that physical fitness was associated with neural activity in right and left superior parietal lobules. Externally defined Regions of Interest confirmed this analysis.

Results indicate frontoparietal hypoactivity in MDD at high demands, arguing for decreased WM capacity. We demonstrate a parietal fitness correlate which could be used to guide future research on effects of exercise on cognitive functioning in MDD.

Altered or atypical functional connectivity as measured with functional magnetic resonance imaging (fMRI) is a hallmark feature of brain connectopathy in psychiatric, developmental, and neurological disorders. However, the biological underpinnings and etiopathological significance of this phenomenon remain unclear. The recent development of MRI-based techniques for mapping brain function in rodents provides a powerful platform to uncover the determinants of functional (dys)connectivity, whether they are genetic mutations, environmental risk factors, or specific cellular and circuit dysfunctions. Here, we summarize the recent contribution of rodent fMRI toward a deeper understanding of network dysconnectivity in developmental and psychiatric disorders. We highlight substantial correspondences in the spatiotemporal organization of rodent and human fMRI networks, supporting the translational relevance of this approach. We then show how this research platform might help us comprehend the importance of connectional heterogeneity in complex brain disorders and causally relate multiscale pathogenic contributors to functional dysconnectivity patterns. Finally, we explore how perturbational techniques can be used to dissect the fundamental aspects of fMRI coupling and reveal the causal contribution of neuromodulatory systems to macroscale network activity, as well as its altered dynamics in brain diseases. These examples outline how rodent functional imaging is poised to advance our understanding of the bases and determinants of human functional dysconnectivity.

EEG studies indicated that schizophrenia patients had increased resting-state theta-band functional connectivity, which was associated with negative symptoms. We recently published the first study showing that theta (6 Hz) transcranial alternating current stimulation (tACS) over left prefrontal and parietal cortices during a working memory task for accentuating frontoparietal theta-band synchronization (in-phase theta-tACS) reduced negative symptoms in schizophrenia patients. Here, we hypothesized that in-phase theta-tACS can modulate theta-band large-scale networks connectivity in schizophrenia patients. In this randomized, double-blind, sham-controlled trial, patients received twice-daily, 2 mA, 20-min sessions of in-phase theta-tACS for 5 consecutive weekdays (n = 18) or a sham stimulation (n = 18). Resting-state electroencephalography data were collected at baseline, end of stimulation, and at one-week follow-up. Exact low resolution electromagnetic tomography (eLORETA) was used to compute intra-cortical activity. Lagged phase synchronization (LPS) was used to measure whole-brain source-based functional connectivity across 84 cortical regions at theta frequency (5-7 Hz). EEG data from 35 patients were analyzed. We found that in-phase theta-tACS significantly reduced the LPS between the posterior cingulate (PC) and the parahippocampal gyrus (PHG) in the right hemisphere only at the end of stimulation relative to sham (p = 0.0009, corrected). The reduction in right hemispheric PC-PHG LPS was significantly correlated with negative symptom improvement at the end of the stimulation (r = 0.503, p = 0.039). Our findings suggest that in-phase theta-tACS can modulate theta-band large-scale functional connectivity pertaining to negative symptoms. Considering the failure of right hemispheric PC-PHG functional connectivity to predict improvement in negative symptoms at one-week follow-up, future studies should investigate whether it can serve as a surrogate of treatment response to theta-tACS.

Psychiatric disorders are highly prevalent, often devastating diseases that negatively impact the lives of millions of people worldwide. Although their etiological and diagnostic heterogeneity has long challenged drug discovery, an emerging circuit-based understanding of psychiatric illness is offering an important alternative to the current reliance on trial and error, both in the development and in the clinical application of treatments. Here we review new and emerging treatment approaches, with a particular emphasis on the revolutionary potential of brain-circuit-based interventions for precision psychiatry. Limitations of circuit models, challenges of bringing precision therapeutics to market and the crucial advances needed to overcome these obstacles are presented.

Working memory (WM) deficits are recognized as serious cognitive impairment in patients with major depressive disorder (MDD). This review aims to clarify the effects of impaired WM function in patients with MDD and explore non-invasive and effective treatments that can be adopted in clinical practice. This review (1) synthesizes extant literature examining brain function and brain areas in terms of WM in individuals with depression, (2) utilizes the outcomes of the studies presented in this review to discuss the effects of impaired WM function on cognitive processing in individuals with depression, (3) integrates the treatments explored in current studies and (4) provides some suggestions for future research. We found that (1) central executive (CE) components affect the processing of WM, and this might be one of the factors influencing cognitive biases, as it is implicated in repetitive negative thinking and rumination; (2) the left dorsal anterior cingulate cortex (dACC), the left dorsolateral prefrontal cortex (DLPFC) and the regions of the default mode network (DMN) play a vital role in CE functioning; and (3) psychotherapy, cognitive training, exercise and physical therapy can be used as complementary treatments for MDD.

Working memory (WM) impairments are common features of psychiatric disorders. A systematic meta-analysis was performed to determine common and disorder-specific brain fMRI response during performance of WM tasks in patients with SZ and patients with MDD relative to healthy controls (HC). Thirty-four published fMRI studies of WM in patients with SZ and 18 published fMRI studies of WM in patients with MDD, including relevant HC, were included in the meta-analysis. In both SZ and MDD there was common stronger fMRI response in right medial prefrontal cortex (MPFC) and bilateral anterior cingulate cortex (ACC), which are part of the default mode network (DMN). The effects were of greater magnitude in SZ than MDD, especially in prefrontal-temporal-cingulate-striatal-cerebellar regions. In addition, a disorder-specific weaker fMRI response was observed in right middle frontal gyrus (MFG) in MDD, relative to HC. For both SZ and MDD a significant correlation was observed between the severity of clinical symptoms and lateralized fMRI response relative to HC. These findings indicate that there may be common and distinct anomalies in brain function underlying deficits in WM in SZ and MDD, which may serve as a potential functional neuroimaging-based diagnostic biomarker with value in supporting clinical diagnosis, measuring illness severity and assessing the efficacy of treatments for SZ and MDD at the brain level.