Brain Imaging and Behavior

Schizophrenia (SZ) is a debilitating disorder in which patients exhibit psychotic behavior due to aberrant connectivity between different regions of the brain. Advances in neuroimaging have now enabled the diagnosis and analysis of SZ in order to elucidate the whole brain functional connectivity networks. In the present study, we have used resting-state functional magnetic resonance imaging (rs-fMRI) to elucidate the causal relationships amongst the differentially activated brain regions between SZ patients (n=10) and healthy controls (n=10). Vector auto-regression (VAR) model and Granger causality (GC) were then applied to construct a functional connectivity network and analyze the causal effects in SZ patients. Our results revealed that the average voxel activation in the frontal lobe (FL), basal ganglia (BG), and ventricular system (VS) was significantly higher in patients indicating hyper-activity as compared to controls. Conversely, cerebellum white matter (CBWM) showed higher activation in the controls as compared to patients. A higher Pearson correlation was observed between the controls as compared to patients while VAR and GC showed higher functional connectivity among all the regions of interest (ROIs) along with more causal relations in the controls. Finally, mediation analysis showed that right middle superior frontal gyrus acts as a strong partial mediator between left accumbens area and left middle superior frontal gyrus. Taken together, this study decodes the dysregulated brain activity in schizophrenia showing hyperactivation in patients when compared with the healthy controls which leads to alterations in neural connections resulting in hypoconnectivity.

Schizophrenia is a psychotic brain disorder in which patients exhibit aberrant connectivity between different regions of the brain. Neuroimaging is a state-of-the-art technique that is now increasingly been employed in clinical investigation of Schizophrenia. In the present study, we have used resting-state functional magnetic resonance neuroimaging (rsfMRI) to elucidate the cause-and-effect relationships among four regions of the brain including occipital, temporal, and frontal lobes and hippocampus in Schizophrenia. For that, we have employed independent component analysis, a seed-based temporal correlation analysis, and Granger causality analysis for measuring causal relationships amongst four regions of the brain in schizophrenia patients. Eighteen subjects with nine patients and nine controls were evaluated in the study. Our results show that Schizophrenia patients exhibit significantly different activation patterns across the selected regions of the brain in comparison with the control. In addition to that, we also observed an aberrant causal relationship between these four regions of the brain. In particular, the temporal and frontal lobes of patients with schizophrenia had a significantly lowered causal relationship with the other areas of the brain. Taken together, the study elucidates the dysregulated brain activity in Schizophrenia patients, decodes its causal mapping and provides novel insights towards employment in clinical evaluation of Schizophrenia.

BackgroundElectroconvulsive Therapy (ECT) is one of the most effective treatments for major depressive disorder (DEP). There is recently increasing attention to evaluate ECTs effect on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of DEP patients with healthy participants, investigate whether dynamic functional network connectivity network (dFNC) estimated from rs-fMRI predicts the ECT outcome, and explore the effect of ECT on brain network states. MethodResting-state fMRI data were collected from 119 patients with depression or DEP (76 females), and 61 Healthy (HC) participants (34 females) with an age mean of 52.25 (N=180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59{+/-}6.14 and 11.48{+/-}9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each participant. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each individual spends in each state, called occupancy rate or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, number of treatment, and site. Finally, we evaluated the effectiveness of ECT by comparing pre-and post-ECT OCR of DEP and HC participants. ResultsThe main findings include: 1) DEP patients had significantly lower OCR values than the HC group in a state, where connectivity between CCN and DMN was relatively higher than other states (corrected p= 0.015), 2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, predicted the HDRS changes (R=0.23 corrected p=0.03). This means that those DEP patients who spend less time in this state showed more HDRS change, and 3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spend in state 2 (corrected p=0.03). Finally, we found ECT increases the total traveled distance in DEP. ConclusionOur finding suggests that dFNC features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identified a possible underlying mechanism associated with the ECT effect in DEP patients.

The neurotic personality has an impact on the regulation of basic negative emotions such as anger, fear, and sadness. There has been extensive research in search of functional connectivity biomarkers of neuroticism and basic negative emotions but there is a lack of research based on effective connectivity. In the current research, we intended to determine the significance of causal interaction of three large-scale resting-state networks - default mode, salience, and executive networks - to predict neuroticism and basic negative emotions. In this study, a large-scale human connectome project dataset comprising functional MRI scans and self-reported scores of neuroticism and negative emotions of 1079 subjects, was utilized. Spectral dynamic causal modelling and parametric empirical Bayes was used to estimate the subject-level effective connectivity parameters and their group-level associations with the neuroticism and emotional scores. Leave-one-out cross-validation using parametric empirical Bayes was employed for prediction analysis. Our results for heightened emotions showed that the self-connection of right hippocampus can predict individuals with high fear, self-connections of dorsal anterior cingulate cortex, posterior cingulate cortex and left dorsolateral prefrontal cortex can predict individuals with high sadness. High anger, low sadness, and neuroticism scores of any emotion category except low fear, could not be predicted using triple network effective connectivity. Our findings revealed that the causal (directed) connections of the resting-state triple network can potentially serve as a connectomic signature for people with high and low fear, high sadness, low anger, and neuroticism with low fear.

Impulsivity and thought suppression are two psychological traits that have great variation in healthy population. In extreme cases, both are closely related to mental illness and play an important role in behavioral addiction. We have known the role of the top-down mechanism in impulsivity and thought suppression, but we do not know how the related neural nuclei are functionally connected and interact with each other. In the study, we selected excessive internet users (EIU) as our target population and investigated the relationship between thought suppression and impulsivity in the following aspects: their correlations to psychological symptoms; the associated neural networks; and the associated brain morphometric changes. We acquired data from 131 excessive internet users, with their psychological, resting-state fMRI and T1-MRI data collected. With the whole brain analysis, graph theory analysis, replication with additional brain atlas, replication with additional MRI data, and analysis of brain structure, we found that: (i) implusivity and thought suppression shared common neural connections in the top-down mechanism; (ii) thought suppression was associated with the neural network that connected to the occipital lobe in the resting-state brain but not the morphometric change of the occipital lobe. The study confirmed the overlap between impulsivity and thought suppression in terms of neural connectivity and suggested the role of thought suppression and the occipital network in behavioral addiction. Studying thought suppression provided a new insight into behavioral addiction research. The neural network study helped further understanding of behavioral addiction in terms of information interaction in the brain.

Investigating brain circuitry involved in bipolar disorder (BD) is key to discovering brain biomarkers for genetic and interventional studies of the disorder. Even so, prior research has not provided a fine-scale spatial mapping of brain microstructural differences in BD. In this pilot diffusion MRI dataset, we used BUndle ANalytics (BUAN), a recently developed analytic approach for tractography, to extract, map, and visualize the profile of microstructural abnormalities on a 3D model of fiber tracts in people with BD (N=38) and healthy controls (N=49), and investigate along-tract white matter (WM) microstructural differences between these groups. Using the BUAN pipeline, BD was associated with lower mean Fractional Anisotropy (FA) in fronto-limbic and interhemispheric pathways and higher mean FA in posterior bundles relative to controls. BUAN combines tractography and anatomical information to capture distinct along-tract effects on WM microstructure that may aid in classifying diseases based on anatomical differences.

Diffusion tensor imaging revealed that trait anxiety predicts the microstructural properties of fiber tracts between the anterior cingulate cortex, prefrontal cortex, and amygdala. However, the whole-brain structural connectivity has been rarely reported in the non-clinical populations with marginal deviations in trait anxiety and substantial changes in state anxiety. This work is aimed to assess the correlation of state and trait anxiety inventory (STAI) scores and their one-day deviation with white matter connectivity at the whole-brain scale. 64-direction diffusion-weighted images were collected in 25 participants without prior complaints of excess anxiety and/or clinical history of anxiety disorders. The self-reported ranking of STAI was collected twice with 24-hour interval: one day before and several minutes before the MRI scanning. A correlation analysis between the generalized fractional anisotropy (GFA) and the STAI ratings was performed for all regions of the brain. According to the diffusion connectometry, the most reproducible positive correlation of GFA and anxiety scores was observed for corpus callosum. For both days of psychological assessment, the left cingulum GFA correlated negatively with State anxiety ratings, while the right cingulum GFA was strongly associated with Trait anxiety. Our results suggest that the density of the corpus callosum and bilateral cingulum tracts are associated with the individual level of anxiety.

Coordinate based meta-analysis (CBMA) can be used to estimate where a future neuroimaging study testing a particular hypothesis might report summary results (activation foci, for example). However, current methods cannot be validated for all possible analyses because of empirical features that might not be appropriate. Furthermore, the use of voxel-wise null hypothesis significance testing (NHST) in the algorithms is not in keeping with meta-analysis, where statistical significance is secondary to the primary aim of effect estimation. Cross-validation coordinate analysis (CVCA) has been described, which can eliminate the need for the empirical use of spatial uncertainty and avoid voxel-wise p values. The result is an estimated effect that is not based on voxel-wise statistical significance, and which allows the uncertainty to reduce with larger numbers of studies as expected. Here an additional function of CVCA is detailed, which uses cross-validation to contrast two different meta-analyses produced using two sets of studies (A & B) to identify differences. Such contrast analysis is common in CBMA. The results are a contrast image with regions that differentiate studies A from studies B. Software to perform CVCA is freely available.

Increasing evidence shows that the brain is a target of SARS-CoV-2. However, the consequences of the virus on the cortical regions of hospitalized patients are currently unknown. The purpose of this study was to assess brain cortical gray matter volume (GMV), thickness (Th), and surface area (SA) characteristics in SARS-CoV-2 hospitalized patients with a wide range of neurological symptoms and their association with clinical indicators of inflammatory processes. A total of 33 patients were selected from a prospective, multicenter, cross-sectional study during the ongoing pandemic (August 2020-April 2021) at Basel University Hospital. Retrospectively biobank healthy controls with the same image protocol served as controls group. For each anatomical T1w MPRAGE image, the Th and GMV segmentation were performed with the FreeSurfer-5.0. Cortical measures were compared between groups using a linear regression model. The covariates were age, gender, age*gender, MRI magnetic field strength, and total intracranial volume/mean Th/Total SA. The association between cortical features and laboratory variables was assessed using partial correlation adjusting for the same covariates. P-values were adjusted using false discovery rate (FDR). Our findings revealed a lower cortical gray matter volume in orbitofrontal and cingulate regions in patients compared to controls. The orbitofrontal grey matter volume was negatively associated with protein levels, CSF-blood/albumin ratio and CSF EN-RAGE level. CSF EN-RAGE and CSF/Blood-albumin ratio, which are neuroinflammatory biomarkers, were associated with cortical alterations in gray matter volume and thickness in frontal, orbitofrontal, and temporal regions. Our data suggest that viral-triggered inflammation leads to increased neurotoxic damage in some cortical areas.

Sex differences in effective brain connectivity in emotional intelligence, emotional regulation, and stimuli-induced negative emotions have been highlighted in previous research. However, to our knowledge, no research has yet investigated the sex-specific effective connectivity related to negative emotions in healthy population during resting-state. The goal of this study is to find the association between sex-specific resting-state effective brain connectivity and basic negative emotions. For this, we have employed the NIH emotion battery of the three self-reported, basic negative emotions -- anger-affect, fear-affect, and sadness which we divided into high, moderate, and low emotion scores in each. The dataset comprises 1079 subjects (584 females) from HCP Young Adults. We selected large-scale resting-state brain networks important for emotional processing namely default mode, executive, and salience networks. We employed subject-level analysis using spectral dynamic causal modelling and group-level association analyses using parametric empirical Bayes. We report association of the self-connection of left hippocampus in females in high anger-affect, fear-affect, and sadness, whereas in males we found involvement of dorsal anterior cingulate cortex (dACC) in all three negative emotions - association of right amygdala to dACC in high anger-affect, association of the self-connection of dACC in high fear-affect, and association of dACC to left hippocampus in high sadness. Our findings primarily revealed the effective brain connectivity that is related to the higher levels of negative emotions that may lead to psychiatric disorders if not regulated. Sex-specific therapies and interventions that target psychopathology can be more beneficial when informed by the sex-specific resting-state effective connectivity.

Mood disorders, including Major Depressive (MDD) and Bipolar (BD) Disorder, are highly prevalent and debilitating conditions that contribute significantly to the global disease burden. These disorders are characterized by persistent emotional dysregulations, such as pervasive sadness and anhedonia, resulting in substantial functional impairments. Although neuroimaging studies have identified differences in brain activity and connectivity between individuals with MDD (MDDs) or BD (BDs) and healthy controls (HCs), reliable and reproducible neurofunctional markers for clinical diagnosis and treatment remain elusive. This study seeks to address this gap by introducing a novel approach that utilizes Divergent Emotional Functional Networks (DEFN), derived from functional magnetic resonance imaging (fMRI) during dynamic emotional processing in naturalistic contexts. Using a combination of naturalistic induction of sustained emotional experience with dynamic functional connectivity (dFC) and machine learning techniques, we decoded emotion-specific patterns of happiness and sadness in healthy individuals. Based on the dynamic functional connectivity signatures, we identify the DEFN and applied it to large clinical mood disorder datasets, including MDD (n=63) and BD patients (n=61). The model with DEFN demonstrated significant improvements in classification accuracy compared to conventional baseline models, achieving up to 10.75% and 9.92% performance increases in MDD and BD datasets, respectively. Additionally, DEFN were found to be highly reproducible across age, gender and models from emotion dataset, supporting the robustness of this model in distinguishing mood disorders from healthy controls. In conclusion, the DEFN approach presents a promising, reproducible, and clinically relevant neural marker for diagnosing and understanding emotional dysfunction in mood disorders, offering potential for more effective and timely interventions.

BackgroundThe pathological mechanisms of Major depressive disorders (MDD) is associated with over-expressing of negative emotions, and the overall temporal-spatial patterns underlying over-representation in depression still remained to be revealed to date. We hypothesized that the aberrant spatio-temporal attributes of the process of sad expressions relate to MDD and help to detect depression severity. MethodsWe enrolled a total of 96 subjects including 47 MDDs and 49 healthy controls (HCs), and recorded their Magnetoencephalography data under a sad expressions recognition task. A hidden Markov model (HMM) was applied to separate the whole neural activity into several brain states, then to characterize the dynamics. To find the disrupted spatial-temporal features, power estimations and fractional occupancy of each state were contrasted between MDDs and HCs. ResultsThree states were found over the period of emotional stimuli processing procedure. The visual state was mainly distributed in early stage (0 - 270ms) and the limbic state in middle and later stage (270ms - 600ms) of the task, while the fronto-parietal state remained a steady proportion across the whole period. MDDs activated statistically more in limbic system during limbic state (p = 0.0045) and less in frontoparietal control network during fronto-parietal state (p = 5.38*10-5) relative to HCs. Hamilton-Depression-Rating Scale scores was significantly correlated with the predicted severity value using the state descriptors (p = 0.0062, r = 0.3933). DiscussionAs human brain exhibited varied activation patterns under the negative stimuli, MDDs expressed disrupted temporal-spatial activated patterns across varied stages involving the primary visual perception and emotional contents processing compared to HCs, indicting disordered regulation of brain functions. Furthermore, descriptors built by HMM could be potential biomarkers for identifying the severity of depression disorders.

The perception of God can be as a transcendent entity that is infinite and outside of human beings, typical for religious traditions, or as an immanent entity that is outside and inside of human beings, typical for mystical traditions. These different perceptions of God may be associated with different neural correlates depending on which God we pray to. To elucidate the neural correlates of these different perceptions of the divine, we compared fMRI activation during praying between 18 Christians and 16 practitioners of Sahaja Yoga Meditation, characterised by transcendent and immanent perceptions of God, respectively. The thalamus was deactivated during praying in Meditators relative to Christians. Due to the sensory relay function of thalamus, the thalamic deactivation in meditators presumably reflects a reduction in the perception of external stimuli in order to focus on the internal perception of an immanent God, while the activation of the thalamus in Christian prayers could be associated with the dialogue with an externally perceived transcendent God.

There are many methods of conducting coordinate based meta-analysis (CBMA) of neuroimaging studies that have tested a common hypothesis. Results are always clusters indicating anatomical regions that are significantly related to the hypothesis. There are limitations such as most methods necessitating the use of conservative family wise error control scheme and the inability to analyse region of interest (ROI) studies, which can be overcome by cluster-wise, rather than voxel-wise, analysis. The false discovery rate error control scheme is a less conservative option suitable for cluster-wise analysis and has the advantage that an easily interpretable error rate is estimated. Furthermore, cluster-wise analysis makes it possible to analyse ROI studies, expanding the pool of data sources. Here a new clustering algorithm for coordinate based analyses is detailed, along with implementation details for ROI studies.

To comprehensively investigate the white matter (WM) features of cerebellum in patients with schizophrenia, and further assess the correlation between altered WM features and clinical and cognitive assessments. Forty-two patients and fifty-two matched healthy controls (HCs) of the Collaborative Informatics and Neuroimaging Suite Data Exchange tool were involved in this study. The cerebellar WM volume was calculated by voxel-based morphometry. And tract-based spatial statistics was used to analysis the diffusion changes in patients when compared to HCs. Furthermore, we investigated the correlation between altered imaging feature and clinical, cognitive assessments. Compared to HCs, the schizophrenia patients did not reveal difference in cerebellar WM volume and schizophrenia patients showed decreased fractional anisotropy and increased radial diffusivity in left middle cerebellar peduncles and inferior cerebellar peduncles in voxel-wise but not in tract-wise. Critically, these cerebellar changes were associated with disease duration in schizophrenia patients. And significant correlation between the altered cerebellar WM features and cognitive assessments only revealed in HCs but disrupted in schizophrenia patients. The present findings suggested that the voxel-wise WM integrity analysis might was the more sensitive way to investigate the structural abnormalities in schizophrenia patients. Middle cerebellar peduncles and inferior cerebellar peduncles may be a crucial neurobiological substrate of cognition and thus might be regarded as a biomarker for treatment.

Investigating alterations in brain circuitry associated with bipolar disorder (BD) may offer a valuable approach to discover brain biomarkers for genetic and interventional studies of the disorder and related mental illnesses. Some diffusion MRI studies report evidence of microstructural abnormalities in white matter regions of interest, but we lack a fine-scale spatial mapping of brain microstructural differences along tracts in BD. We also lack large-scale studies that integrate tractometry data from multiple sites, as larger datasets can greatly enhance power to detect subtle effects and assess whether effects replicate across larger international datasets. In this multisite diffusion MRI study, we used BUndle ANalytics (BUAN, Chandio 2020), a recently developed analytic approach for tractography, to extract, map, and visualize profiles of microstructural abnormalities on 3D models of fiber tracts in 148 participants with BD and 259 healthy controls from 6 independent scan sites. Modeling site differences as random effects, we investigated along-tract white matter (WM) microstructural differences between diagnostic groups. QQ plots showed that group differences were gradually enhanced as more sites were added. Using the BUAN pipeline, BD was associated with lower mean fractional anisotropy (FA) in fronto-limbic, interhemispheric, and posterior pathways; higher FA was also noted in posterior bundles, relative to controls. By integrating tractography and anatomical information, BUAN effectively captures unique effects along white matter (WM) tracts, providing valuable insights into anatomical variations that may assist in the classification of diseases.

Anger can be deconstructed into distinct components: a temporary emotional state (state anger), a stable personality trait (trait anger), a tendency to outwardly express it (anger-out), or to internally suppress it (anger-in), and the capability to manage it (anger control). These aspects exhibit individual differences that vary across a continuum. Notably, the capacity to express and control anger are of great importance to modulate our reactions in interpersonal situations. The aim of this study was to test the hypothesis that anger expression and control are negatively correlated and that both can be decoded by the same patterns of grey and white matter features of a fronto-temporal brain network. To this aim, a data fusion unsupervised machine learning technique, known as transposed Independent Vector Analysis (tIVA), was used to decompose the brain into covarying GM-WM networks and then backward regression was used to predict both anger expression and control from a sample of 212 healthy subjects. Confirming our hypothesis, results showed that anger control and anger expression are negatively correlated, the more individuals control anger, the less they externalize it. At the neural level, individual differences in anger expression and control can be predicted by the same GM-WM network. As expected, this network included fronto-temporal regions as well as the cingulate, the insula and the precuneus. The higher the concentration of GM-WM in this brain network, the higher level of externalization of anger, and the lower the anger control. These results expand previous findings regarding the neural bases of anger by showing that individual differences in anger control and expression can be predicted by morphometric features.

Low statistical power remains a persistent concern in functional magnetic resonance imaging (fMRI) research, largely due to small sample sizes. Although prior work has documented gradual increases in sample size over time, it remains unclear whether structural factors in the publication process are associated with study design characteristics such as sample size. This review addresses this gap by analyzing a large sample of fMRI studies to assess how institutional prestige, journal impact factor, and journal review practices are associated with sample size. We analyzed articles published in 2021-2024 reporting new fMRI data collection in adult humans and including a measure of memory. We found studies with specialized populations, such as patient populations, had smaller sample sizes, as did studies with task-based designs compared to resting-state designs. We also found larger sample sizes were associated with journals with a double-blind review process. Institutional prestige was positively associated with sample size such that more highly ranked institutions tended to have larger samples, but there was no interaction between review type (single-vs. double-blind) and prestige, indicating this difference is not likely due to reviewer bias. Journal impact factor was not associated with sample size, however institutional prestige score predicted journal impact factor. These results suggest structural factors at the institutional level likely have a stronger influence on published study sample size than reviewer practices or biases.

BackgroundCharles Bonnet syndrome (CBS) is a prototype phenomenon for investigating complex visual hallucination. Our research focuses on resting state neural networks features of CBS patients with a comparison of patients with equally matched visual loss and healthy subjects in order to investigate the mechanism behind complex visual hallucinations. Material and MethodsFour CBS patients CBS(+), three patients with visual loss but no visual hallucinations CBS(-) and 15 healthy individuals (HS) undergo resting state fMRI recordings and their resting state data is analyzed for Default Mode Network (DMN) changes through dual regression analysis. Cognitive functions of the participants were also evaluated through Mini Mental State Examination and University of Miami - Parkinsons Disease Hallucination Questionnaire (um-PDHQ) ResultsAlthough we found no difference in Default Mode Networks between CBS(-) and CBS(+), and between the CBS(-) and HC groups, we detected decreased connectivity in CBS(+) compared to the HC group especially in visual heteromodal association centers (bilateral lateral occipital gyrus, bilateral lingual gyrus, occipital pole, right medial temporal cortex, right temporo-occipital cortex) when left angular gyrus was selected as ROI. Similarly, we detected decreased connectivity in CBS(+) compared to HC in right medial frontal gyrus, right posterior cingulate gyrus, left inferior temporal gyrus, right supramarginal gyrus, and right angular gyrus when selected right superior frontal gyrus as ROI. In contrast, increased connectivity was detected in CBS +compared to HC, in bilateral occipital poles, bilateral occipital fusiform gyrus, bilateral intracalcarine cortex, right lingual gyrus and precuneus regions when left medial temporal gyrus was selected as ROI. ConclusionOur findings suggest a combined mechanism in CBS related to increased internal created images caused by decreased visual external input causing visual hallucinations as well as impaired frontotemporal resource tracking system that together impair cognitive processing.

Bipolar disorder (BD) is characterized by dynamic transitions between depressive and manic states, yet the neural mechanisms underlying these state shifts remain unclear. Here, we developed an integrative framework to quantify the microstructural-functional coupling (MFC) of the brain, which captures the distributional similarity between voxel-wise diffusion and functional features within each cortical region. Using multimodal MRI data from 72 BD patients and 65 matched healthy controls, we observed a global increase of MFC of BD, which indicates a de-similarity of brain microstructure and function. Partial least squares correlation (PLSC) analysis revealed that the principal latent component linking MFC to behavioral scores explained 67.2% of the variance, with the strongest contributions from the control and somatosensory networks. Mediation analysis demonstrated that regional MFC influenced anxiety indirectly through depressive symptoms, supporting a sequential affective transition from depression to anxiety. Moreover, macroscale gradient analysis showed that the principal MFC gradient was significantly correlated with neurotransmitter receptor distributions, including 4{beta}2, 5-HT1B, and H3. Together, these findings highlight MFC as a multimodal biomarker sensitive to emotional state transitions in BD and suggest its potential as a bridge between microstructural alterations, functional dynamics, and neurotransmitter systems.