Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis

Identifying Refractory Epilepsy Without Structural Abnormalities by Fusing the Common Spatial Patterns of Functional and Effective EEG Networks

Drug refractory epilepsy (RE) is believed to be associated with structural lesions, but some RE patients show no significant structural abnormalities (RE-no-SA) on conventional magnetic resonance imaging scans. Since most of the medically controlled epilepsy (MCE) patients also do not exhibit structural abnormalities, a reliable assessment needs to be developed to differentiate RE-no-SA patients and MCE patients to avoid misdiagnosis and inappropriate treatment. Using resting-state scalp electroencephalogram (EEG) datasets, we extracted the spatial pattern of network (SPN) features from the functional and effective EEG networks of both RE-no-SA patients and MCE patients. Compared to the performance of traditional resting-state EEG network properties, the SPN features exhibited remarkable superiority in classifying these two groups of epilepsy patients, and accuracy values of 90.00% and 80.00% were obtained for the SPN features of the functional and effective EEG networks, respectively. By further fusing the SPN features of functional and effective networks, we demonstrated that the highest accuracy value of 96.67% could be reached, with a sensitivity of 100% and specificity of 92.86%. Overall, these findings not only indicate that the fused functional and effective SPN features are promising as reliable measurements for distinguishing RE-no-SA patients and MCE patients but also may provide a new perspective to explore the complex neurophysiology of refractory epilepsy.

Support vector machine based classification of smokers and nonsmokers using diffusion tensor imaging

Despite significant progress in treatments for smoking cessation, smoking continues to be a significant public health concern, especially in young adulthood. Thus, developing a predictive model that can classify and characterize the brain-based biomarkers predicting smoking status would be imperative to improving treatment development. In this study, we applied a support vector machine-based classification method to discriminate 70 young male smokers and 70 matched nonsmokers using their diffusion tensor imaging (DTI) data. The classification procedure achieved an average accuracy of 88.6% and an average area under the curve of 0.95. The most discriminative features that contributed to the classification were primarily located in the sagittal stratum (SS), external capsule (EC), superior longitudinal fasciculus (SLF), anterior corona radiata (ACR) and inferior front-occipital fasciculus (IFOF). The following regression analysis showed a significant negatively correlation between the average RD values of the left ACR (r = -0.247, p = 0.039) and FTND. The average MD values in the right EC (r = -0.254, p = 0.034) and RD values in the right IFOF (r = -0.240, p = 0.046) were inversely associated with pack-years. Our findings indicate that the discriminative white matter (WM) features as brain biomarkers provide great predictive power for smoking status and suggest that machine learning techniques can reveal underlying smoking-related neurobiology.

Predicting visual working memory with multimodal magnetic resonance imaging

The indispensability of visual working memory (VWM) in human daily life suggests its importance in higher cognitive functions and neurological diseases. However, despite the extensive research efforts, most findings on the neural basis of VWM are limited to a unimodal context (either structure or function) and have low generalization. To address the above issues, this study proposed the usage of multimodal neuroimaging in combination with machine learning to reveal the neural mechanism of VWM across a large cohort (N = 547). Specifically, multimodal magnetic resonance imaging features extracted from voxel-wise amplitude of low-frequency fluctuations, gray matter volume, and fractional anisotropy were used to build an individual VWM capacity prediction model through a machine learning pipeline, including the steps of feature selection, relevance vector regression, cross-validation, and model fusion. The resulting model exhibited promising predictive performance on VWM (r = .402, p < .001), and identified features within the subcortical-cerebellum network, default mode network, motor network, corpus callosum, anterior corona radiata, and external capsule as significant predictors. The main results were then compared with those obtained on emotional regulation and fluid intelligence using the same pipeline, confirming the specificity of our findings. Moreover, the main results maintained well under different cross-validation regimes and preprocess strategies. These findings, while providing richer evidence for the importance of multimodality in understanding cognitive functions, offer a solid and general foundation for comprehensively understanding the VWM process from the top down.

The early postpartum period - Differences between women with and without a history of depression

Depression is a highly recurrent disorder. When in remission, it affords an important opportunity to understand the state-independent neurobiological alterations, as well as the socio-demographic characteristics, that likely contribute to the recurrence of major depressive disorder (MDD). The present study examined 110 euthymic women in their early postpartum period. A comparison was made between participants with (n = 20) and without (n = 90) a history of MDD by means of a multimodal approach including an fMRI experiment, assessment of hair cortisol concentration (HCC) and a clinical anamnestic interview. Women with a personal history of MDD were found to have decreased resting-state functional connectivity (RSFC) between the lateral parietal cortex (LPC) and the posterior cingulate cortex (PCC), and their Edinburgh Postnatal Depression Scale (EPDS) scores were significantly higher shortly after childbirth. More often than not, these women also had a family history of MDD. While women with no history of depression showed a negative association between hair cortisol concentration (HCC) and gray matter volume (GMV) in the medial orbitofrontal cortex (mOFC), the opposite trend was seen in women with a history of depression. This implies that women with remitted depression show distinctive neural phenotypes with subclinical residual symptoms, which likely predispose them to later depressive episodes.

Oxytocin effects on the resting-state mentalizing brain network

Oxytocin (OT) has modulatory effects in both human behavior and in the brain, which is not limited in the specific brain area but also with the potential effect on connectivity with other brain regions. Evidence indicates that OT effects on human behavior are multifaceted, such as trust behavior, decrease anxiety, empathy and bonding behavior. For the vital role of mentalizing in understanding others, here we examine whether OT has a general effect on mentalizing brain network which is associated to the effect of related social behavioral and personality traits. Using a randomized, double-blind placebo-controlled group design, we investigate the resting-state functional magnetic resonance imaging after intranasal OT or placebo. The functional connectivity (FC) maps with seed in left/right temporoparietal junction (lTPJ/rTPJ) showed that OT significantly increased connectivity between rTPJ and default attention network (DAN), but decreased the FC between lTPJ and medial prefrontal network (MPN). With machine learning approach, we report that identified altered FCs of TPJ can classify OT and placebo (PL) group. Moreover, individual's empathy trait can modulate the FC between left TPJ and right rectus (RECT), which shows a positive correlation with empathic concern in PL group but a negative correlation in OT group. These results demonstrate that OT has significant effect on FC with lTPJ and rTPJ, brain regions where are critical for mentalizing, and the empathy concern can modulate the FC. These findings advance our understanding of the neural mechanisms by which OT modulates social behaviors, especially in social interaction involving mentalizing.

A review of resting-state fMRI and its use to examine psychiatric disorders

Resting-state fMRI (rs-fMRI) has emerged as an alternative method to study brain function in human and animal models. In humans, it has been widely used to study psychiatric disorders including schizophrenia, bipolar disorder, autism spectrum disorders, and attention deficit hyperactivity disorders. In this review, rs-fMRI and its advantages over task based fMRI, its currently used analysis methods, and its application in psychiatric disorders using different analysis methods are discussed. Finally, several limitations and challenges of rs-fMRI applications are also discussed.

The Assessment of Cognitive Dysfunction in Major Depressive Disorder: A 16-Week Prospective Case-Control Study

OBJECTIVE

Cognitive dysfunction is one of the core components of major depressive disorder (MDD). It is estimated that two-thirds of patients diagnosed with MDD have cognitive deficits. Cognitive symptoms are pervasive and affect functioning in several domains. This 16-week prospective case-control study aimed to assess the change of mood and cognitive symptoms during treatment.

MATERIALS AND METHODS

Ninety-eight patients with MDD and 113 healthy controls (HCs) participated in the study. The MDD group was evaluated 6 times (baseline, 2nd, 4th, 8th, 12th, and 16th weeks). For mood symptoms, the Montgomery-Asberg Depression Rating Scale was used, and for neurocognitive functions, the Perceived Deficits Questionnaire-Depression was used, and the Digit Symbol Substitution Test was administered to both groups.

RESULTS

At baseline, compared with the HCs, the neurocognitive function of patients with MDD was worse. From the 8th to the 16th week assessments, in both neurocognitive tests, the cognitive functions of patients with MDD had improved. Despite this improvement and the patients achieving remission, the patients' cognitive performance did not improve to the level of the HC group at the 16th week.

CONCLUSION

Our longitudinal research revealed that even though mood symptoms decreased and patients with depression did achieve symptomatic remission, their cognitive deficits perpetuated.

Cerebral amyloid accumulation is associated with distinct structural and functional alterations in the brain of depressed elders with mild cognitive impairment

BACKGROUND

Elderly patients with late-life depression (LLD) often report mild cognitive impairment (MCI), so Alzheimer's disease (AD) is hard to identify in these patients. We aimed to identify the structural and functional differences between prodromal AD and LLD-related MCI.

METHODS

We performed voxel-based morphometry and functional connectivity (FC) analyses in elderly patients with both LLD and MCI to compare alterations between those with cerebral amyloidopathy and those without. We subdivided patients into subthreshold depression (STD) and major depressive disorder (MDD) groups. Using florbetaben positron emission tomography (PET), we compared volume and connectivity between healthy controls and four STD and MDD groups with or without amyloid deposition(A): STD-MCI-A(+), MDD-MCI-A(+), STD-MCI-A(-), and MDD-MCI-A(-).

RESULTS

Subjects with MDD or amyloid deposition showed greater volume reduction in the left middle temporal gyrus. MDD groups had lower FC than STD groups in the frontal, cortical, and limbic areas. The STD-MCI-A(+) group showed greater FC reduction than the MDD-MCI-A(-) and STD-MCI-A(-) groups, particularly in the hippocampus, parahippocampus, and frontal and temporal cortices. The functional differences associated with amyloid plaques were more evident in the STD group than in the MDD group.

LIMITATIONS

Limitations include disproportional sex ratios, inability to determine the longitudinal effects of amyloidopathy in large populations.

CONCLUSIONS

Regional gray matter loss and alterations in brain networks may reflect impairments caused by amyloid deposition and depression. Such changes may facilitate the detection of prodromal AD in elderly patients with both depression and cognitive dysfunction, allowing earlier intervention and more appropriate treatment.

Default mode network activity in depression subtypes

Depression continues to carry a major disease burden worldwide, with limitations on the success of traditional pharmacological or psychological treatments. Recent approaches have therefore focused upon the neurobiological underpinnings of depression, and on the "individualization" of depression symptom profiles. One such model of depression has divided the standard diagnostic criteria into four "depression subtypes", with neurological and behavioral pathways. At the same time, attention has been focused upon the region of the brain known as the "default mode network" (DMN) and its role in attention and problem-solving. However, to date, no review has been published of the links between the DMN and the four subtypes of depression. By searching the literature studies from the last 20 years, 62 relevant papers were identified, and their findings are described for the association they demonstrate between aspects of the DMN and the four depression subtypes. It is apparent from this review that there are potential positive clinical and therapeutic outcomes from focusing upon DMN activation and connectivity, via psychological therapies, transcranial magnetic stimulation, and some emerging pharmacological models.

Functional plasticity abnormalities over the lifespan of first-episode patients with major depressive disorder: a resting state fMRI study

Background

Neurodevelopmental and neurodegenerative theories of depression suggest that patients with major depressive disorder (MDD) may follow abnormal developmental, maturational, and aging processes. However, a lack of lifespan studies has precluded verification of these theories. Herein, we analyzed functional magnetic resonance imaging (fMRI) data to comprehensively characterize age-related functional trajectories, as measured by the fractional amplitude of low frequency fluctuations (fALFF), over the course of MDD.

Methods

In total, 235 MDD patients with age-differentiated onsets and 235 age- and sex-matched healthy controls (HC) were included in this study. We determined the pattern of age-related fALFF changes by cross-sectionally establishing the general linear model (GLM) between fALFF and age over a lifespan. Furthermore, the subjects were divided into four age groups to assess age-related neural changes in detail. Inter-group fALFF comparison (MDD vs. HC) was conducted in each age group and Granger causal analysis (GCA) was applied to investigate effective connectivity between regions.

Results

Compared with the HC, no significant quadratic or linear age effects were found in MDD over the entire lifespan, suggesting that depression affects the normal developmental, maturational, and degenerative process. Inter-group differences in fALFF values varied significantly at different ages of onset. This implies that MDD may impact brain functions in a highly dynamic way, with different patterns of alterations at different stages of life. Moreover, the GCA analysis results indicated that MDD followed a distinct pattern of effective connectivity relative to HC, and this may be the neural basis of MDD with age-differentiated onsets.

Conclusions

Our findings provide evidence that normal developmental, maturational, and ageing processes were affected by MDD. Most strikingly, functional plasticity changes in MDD with different ages of onset involved dynamic interactions between neuropathological processes in a tract-specific manner.

Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies

Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.

Altered Fractional Amplitude of Low-Frequency Fluctuation in Major Depressive Disorder and Bipolar Disorder

Background: Discriminating between major depressive disorder (MDD) and bipolar disorder (BD) remains challenging and cognitive deficits in MDD and BD are generally recognized. In this study, the fractional amplitude of low-frequency fluctuation (fALFF) approach was performed to explore neural activity and cognition in first-episode, drug-naïve BD and MDD patients, as well as the relationship between altered fALFF values and clinical or psychometric variables. Methods: A total of 21 BD patients, 25 MDD patients, and 41 healthy controls (HCs) completed clinical assessments and resting-state functional magnetic resonance imaging (rs-fMRI) scans in this study. The rs-fMRI data were analyzed by fALFF method and Pearson correlation analyses were performed between altered fALFF values and clinical variables or cognition. Support vector machine (SVM) was adopted to identify the three groups from each other with abnormal fALFF values in the brain regions obtained by group comparisons. Results: (1) The fALFF values were significantly different in the frontal lobe, temporal lobe, and left precuneus among three groups. In comparison to HCs, BD showed increased fALFF values in the right inferior temporal gyrus (ITG) and decreased fALFF values in the right middle temporal gyrus, while MDD showed decreased fALFF values in the right cerebellar lobule IV/V. In comparison to MDD, BD showed decreased fALFF values in bilateral posterior cingulate gyrus and the right cerebellar lobule VIII/IX. (2) In the BD group, a negative correlation was found between increased fALFF values in the right ITG and years of education, and a positive correlation was found between decreased fALFF values in the right cerebellar lobule VIII/IX and visuospatial abilities. (3) The fALFF values in the right cerebellar lobule VIII/IX may have the ability to discriminate BD patients from MDD patients, with sensitivity, specificity, and accuracy all over 0.70. Conclusions: Abnormal brain activities were observed in BD and MDD and were related with cognition in BD patients. The abnormality in the cerebellum can be potentially used to identify BD from MDD patients.

Transcranial magnetic stimulation neurophysiology of patients with major depressive disorder: a systematic review and meta-analysis

Major depressive disorder (MDD) is a mental illness with high socio-economic burden, but its pathophysiology has not been fully elucidated. Recently, the cortical excitatory and inhibitory imbalance hypothesis and neuroplasticity hypothesis have been proposed for MDD. Although several studies have examined the neurophysiological profiles in MDD using transcranial magnetic stimulation (TMS), a meta-analysis of TMS neurophysiology has not been performed. The objective of this study was to compare TMS-electromyogram (TMS-EMG) findings between patients with MDD and healthy controls (HCs). To this end, we examined whether patients with MDD have lower short-interval cortical inhibition (SICI) which reflects gamma-aminobutyric acid (GABA)A receptor-mediated activity, lower cortical silent period (CSP) which represents GABAB receptor-mediated activity, higher intracortical facilitation (ICF) which reflects glutamate N-methyl-D-aspartate receptor-mediated activity, and the lower result of paired associative stimulation (PAS) paradigm which shows the level of neuroplasticity in comparison with HC. Further, we explored the effect of clinical and demographic factors that may influence TMS neurophysiological indices. We first searched and identified research articles that conducted single- or paired-pulse TMS-EMG on patients with MDD and HC. Subsequently, we extracted the data from the included studies and meta-analyzed the data with the comprehensive meta-analysis software. Patients with MDD were associated with lower SICI, lower CSP, potentially higher ICF, and lower PAS compared with HC. Our results confirmed the proposed hypotheses, suggesting the usefulness of TMS neurophysiology as potential diagnostic markers of MDD.

Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts

Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD.

Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms

BACKGROUND

Functional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Functional disruption during infancy could provide earlier markers of ASD, thus providing a crucial opportunity to improve developmental outcomes. Using a whole-brain multivariate approach, we asked whether electroencephalography measures of neural connectivity at 3 months of age predict autism symptoms at 18 months.

METHODS

Spontaneous electroencephalography data were collected from 65 infants with and without familial risk for ASD at 3 months of age. Neural connectivity patterns were quantified using phase coherence in the alpha range (6-12 Hz). Support vector regression analysis was used to predict ASD symptoms at age 18 months, with ASD symptoms quantified by the Toddler Module of the Autism Diagnostic Observation Schedule, Second Edition.

RESULTS

Autism Diagnostic Observation Schedule scores predicted by support vector regression algorithms trained on 3-month electroencephalography data correlated highly with Autism Diagnostic Observation Schedule scores measured at 18 months (r = .76, p = .02, root-mean-square error = 2.38). Specifically, lower frontal connectivity and higher right temporoparietal connectivity at 3 months predicted higher ASD symptoms at 18 months. The support vector regression model did not predict cognitive abilities at 18 months (r = .15, p = .36), suggesting specificity of these brain patterns to ASD.

CONCLUSIONS

Using a data-driven, unbiased analytic approach, neural connectivity across frontal and temporoparietal regions at 3 months predicted ASD symptoms at 18 months. Identifying early neural differences that precede an ASD diagnosis could promote closer monitoring of infants who show signs of neural risk and provide a crucial opportunity to mediate outcomes through early intervention.

Connectome-Based Prediction of Optimal Weight Loss Six Months After Bariatric Surgery

Despite bariatric surgery being the most effective treatment for obesity, a proportion of subjects have suboptimal weight loss post-surgery. Therefore, it is necessary to understand the mechanisms behind the variance in weight loss and identify specific baseline biomarkers to predict optimal weight loss. Here, we employed functional magnetic resonance imaging (fMRI) with baseline whole-brain resting-state functional connectivity (RSFC) and a multivariate prediction framework integrating feature selection, feature transformation, and classification to prospectively identify obese patients that exhibited optimal weight loss at 6 months post-surgery. Siamese network, which is a multivariate machine learning method suitable for small sample analysis, and K-nearest neighbor (KNN) were cascaded as the classifier (Siamese-KNN). In the leave-one-out cross-validation, the Siamese-KNN achieved an accuracy of 83.78%, which was substantially higher than results from traditional classifiers. RSFC patterns contributing to the prediction consisted of brain networks related to salience, reward, self-referential, and cognitive processing. Further RSFC feature analysis indicated that the connection strength between frontal and parietal cortices was stronger in the optimal versus the suboptimal weight loss group. These findings show that specific RSFC patterns could be used as neuroimaging biomarkers to predict individual weight loss post-surgery and assist in personalized diagnosis for treatment of obesity.

Dimensions of pain catastrophising and specific structural and functional alterations in patients with chronic pain: Evidence in medication-overuse headache

OBJECTIVES

We examined the neuroanatomical substrate of different pain catastrophising (PC) dimensions (i.e. rumination; magnification; helplessness) in patients with medication-overuse headache (MOH).

METHODS

We included 18 MOH patients who were administered the Pain Catastrophizing Scale (PCS) and scanned in a 3T-MRI. We conducted whole-brain volumetric and resting-state functional connectivity (FC) analysis to examine the association between grey matter (GM) density and FC strength and PCS dimensions controlling for depression and anxiety.

RESULTS

Higher total PCS score was associated with decreased GM density in precentral and inferior temporal gyrus, increased FC between middle temporal gyrus and cerebellum and reduced FC between precuneus and inferior temporal gyrus, as well as between frontal pole and temporal fusiform cortex. Regarding PCS dimensions, we mainly observed the involvement of (1) somatosensory cortex, supramarginal gyrus, basal ganglia, core default-mode network (DMN) in rumination; (2) somatosensory cortex, core DMN, dorsal medial prefrontal cortex (DMPFC)-DMN subsystem and cerebellum in magnification; and (3) temporal regions, DMN and basal ganglia in helplessness.

CONCLUSIONS

PC dimensions are associated with a specific structural and functional neuroanatomical pattern, which is different from the pattern observed when PC is considered as a single score. The involvement of basal ganglia and cerebellum needs further investigation.

Abnormal rich club organization in end-stage renal disease patients before dialysis initiation and undergoing maintenance hemodialysis

Background

End-stage renal disease (ESRD) patients are at a substantially higher risk for developing cognitive impairment compared with the healthy population. Dialysis is an essential way to maintain the life of ESRD patients. Based on previous research, there isn’t an uncontested result whether cognition was improved or worsened during dialysis.

Methods

To explore the impact of dialysis treatment on cognitive performance, we recruited healthy controls (HCs), predialysis ESRD patients (predialysis group), and maintenance hemodialysis ESRD patients (HD group). All ESRD patients performed six blood biochemistry tests (hemoglobin, urea, cystatin C, Na+, K+, and parathyroid hormone). Neuropsychological tests were used to measure cognitive function. By using diffusion tensor imaging and graph-theory approaches, the topological organization of the whole-brain structural network was investigated. Generalized linear models (GLMs) were performed to investigate blood biochemistry predictors of the neuropsychological tests and the results of graph analyses in the HD group and predialysis group.

Results

Neuropsychological analysis showed the HD group exhibited better cognitive function than the predialysis group, but both were worse than HCs. Whole-brain graph analyses revealed that increased global efficiency and normalized shortest path length remained in the predialysis group and HD group than the HCs. Besides, a lower normalized clustering coefficient was found in the predialysis group relative to the HCs and HD group. For the GLM analysis, only the Cystatin C level was significantly associated with the average fiber length of rich club connections in the predialysis group.

Conclusions

Our study revealed that dialysis had a limited effect on cognitive improvement.

Lesions in the right Rolandic operculum are associated with self-rating affective and apathetic depressive symptoms for post-stroke patients

Stroke survivors majorly suffered from post-stroke depression (PSD). The PSD diagnosis is commonly performed based on the clinical cut-off for psychometric inventories. However, we hypothesized that PSD involves spectrum symptoms (e.g., apathy, depression, anxiety, and stress domains) and severity levels. Therefore, instead of using the clinical cut-off, we suggested a data-driven analysis to interpret patient spectrum conditions. The patients’ psychological conditions were categorized in an unsupervised manner using the k-means clustering method, and the relationships between psychological conditions and quantitative lesion degrees were evaluated. This study involved one hundred sixty-five patient data; all patients were able to understand and perform self-rating psychological conditions (i.e., no aphasia). Four severity levels—low, low-to-moderate, moderate-to-high, and high—were observed for each combination of two psychological domains. Patients with worse conditions showed the significantly greater lesion degree at the right Rolandic operculum (part of Brodmann area 43). The dissimilarities between stress and other domains were also suggested. Patients with high stress were specifically associated with lesions in the left thalamus. Impaired emotion processing and stress-affected functions have been frequently related to those lesion regions. Those lesions were also robust and localized, suggesting the possibility of an objective for predicting psychological conditions from brain lesions.

Gradually evaluating of suicidal risk in depression by semi-supervised cluster analysis on resting-state fMRI

A timely and effective evaluation of the suicidal ideation bears practical meaning, particularly for the depressive who tend to disguise the real suicide intent and without obvious symptoms. Measuring individual ideation of the depression with uncertain or transient suicide crisis is the purpose. Resting-state fMRI data were collected from 78 depressed patients with variable clinical suicidal crisis. Thirty subjects were well labeled as extremely serious individuals with suicide attempters or as without suicidal ideation. A feature mask was constructed via the two sample t-test on their regional conncectivities. Then, a semi-supervised machine learning frame using the feature mask was designed to assist in clarifying gradation of suicidal susceptibility for the residual forty-eight vaguely defined subjects, by a way of Iterative Self-Organizing Data analysis techniques (ISODATA). Such semi-supervised model was designed purposely to block out the effect of disease itself on the suicide intendancy evaluation. The vague-labeled patients were divided into another two different stages relating to their suicidal susceptibility. The distance ratio of each subject to the two well-defined extreme groups in the feature space can be utilized as the suicide risk index. The re-evaluation of the Nurses' Global Assessment of Suicide Risk (NGASR) via experts blind to original HAM-D rates was significantly correlated with the model estimation. The constructed model suggested its potential to examine the risk of suicidal in an objective way. The functional connectivity, locating mostly within the frontal-temporal circuit and involving the default mode network (DMN), were well integrated to discriminative the gradual susceptibility of suicidal.

Cannabidiol normalizes resting-state functional connectivity in treatment-resistant epilepsy

OBJECTIVE

Resting-state (rs) network dysfunction is a contributing factor to treatment resistance in epilepsy. In treatment-resistant epilepsy (TRE), pharmacological and nonpharmacological therapies have been shown to improve such dysfunction. In this study, our goal was to prospectively evaluate the effect of highly purified plant-derived cannabidiol (CBD; Epidiolex®) on rs functional magnetic resonance imaging (fMRI) functional connectivity (rs-FC). We hypothesized that CBD would change and potentially normalize the rs-FC in TRE.

METHODS

Twenty-two of 27 participants with TRE completed all study procedures including longitudinal pre-/on-CBD rs-fMRI (8M/14F, mean age = 36.2 ± 15.9 years, TRE duration = 18.3 ± 12.6 years); there were no differences in age (p = 0.99) or sex (p = 0.15) between groups. Assessments collected included seizure frequency (SF), Chalfont Seizure Severity Scale (CSSS), Columbia Suicide Severity Rating Scale (C-SSRS), Adverse Events Profile (AEP), and Profile of Mood States (POMS). Twenty-three healthy controls (HCs) received rs-fMRI and POMS once.

RESULTS

Participants with TRE showed average decrease of 71.7% in SF (p < 0.0001) and improved CSSS, AEP, and POMS confusion, depression, and fatigue subscores (all p < 0.05) on-CBD with POMS scores becoming similar to those of HCs. Paired t-tests showed significant pre-/on-CBD changes in rs-FC in cerebellum, frontal areas, temporal areas, hippocampus, and amygdala with some of them correlating with improvement in behavioral measures. Significant differences in rs-FC between pre-CBD and HCs were found in cerebellum, frontal, and occipital regions. After controlling for changes in SF with CBD, these differences were no longer present when comparing on-CBD to HCs.

SIGNIFICANCE

This study indicates that highly purified CBD modulates and potentially normalizes rs-FC in the epileptic brain. This effect may underlie its efficacy. This study provides Class III evidence for CBD's normalizing effect on rs-FC in TRE.

All roads lead to the default-mode network-global source of DMN abnormalities in major depressive disorder

Major depressive disorder (MDD) is a psychiatric disorder characterized by abnormal resting state functional connectivity (rsFC) in various neural networks and especially in default-mode network (DMN). However, inconsistent findings, i.e., increased and decreased DMN rsFC, have been reported, which raise the question for the source of DMN changes in MDD. Testing whether the DMN abnormalities in MDD can be traced to either a local, i.e., intra-network, or a global, i.e., inter-network, source, we conducted a novel sequence of rsFC analyses, i.e., global FC, intra-network FC, and inter-network FC. Moreover, all analyses were conducted without global signal regression (non-GSR) and with GSR in order to identify the impact of specifically the global component of functional connectivity on within-network functional connectivity within specifically the DMN. In MDD our findings demonstrate (i) increased representation of global signal correlation (GSCORR) in DMN regions, as confirmed independently by degree of centrality (DC) and by an independent DMN template, (ii) increased within-network DMN rsFC, (iii) highly increased inter-network rsFC of both lower- and higher order non-DMN networks with DMN, (iv) high accuracy in classifying MDD vs. healthy subjects by using GSCORR as predictor. Further supporting the global, i.e., non-DMN source of within-network rsFC of the DMN, all results were obtained only when including the global signal, i.e., non-GSR, but not when conducting GSR. Together, we show for the first time increased global signal representation within rsFC of DMN as stemming from inter-network sources as distinguished from local sources, i.e., within- or intra-DMN.

Neural correlates of the association between depression and high density lipoprotein cholesterol change

There is evidence that major depressive disorder (MDD) is related to serum lipid level alterations. However, the neural correlates underlying this association remain poorly understood. Forty-nine patients with MDD and fifty healthy controls (HCs) underwent structural, resting-state functional and diffusion magnetic resonance imaging scans. Voxel-based morphometry, functional connectivity (FC) and tract-based spatial statistics analyses were performed to assess brain structure and function, respectively. Blood samples were collected to measure serum levels of lipid variables including total cholesterol, triglyceride and high density lipoprotein cholesterol (HDL-C). Correlation and mediation analyses were conducted to investigate the associations of serum lipid levels with brain imaging measures in MDD patients and HCs, respectively. We found that the serum HDL-C level in MDD patients was lower than that in HCs. The lower serum HDL-C level was associated with lower gray matter volume (GMV) in ventromedial prefrontal cortex (VMPFC), higher within-network FC of the default mode network, and lower micro-structural integrity in multiple white matter regions in MDD patients. Moreover, the within-default mode network FC mediated the relationship between GMV in VMPFC and serum HDL-C level; white matter integrity in genu of corpus callosum mediated the relationship between serum HDL-C level and depressive symptom severity. However, we did not observe any correlations between serum lipids and brain imaging parameters in HCs. These findings help to identify neural correlates underlying the association between depression and serum HDL-C change, which may provide new insight into intervention, treatment and prevention of depression from the perspective of regulating serum lipids.

Discrimination of Tourette Syndrome Based on the Spatial Patterns of the Resting-State EEG Network

Tourette syndrome (TS) is a neuropsychiatric disorder with childhood onset characterized by chronic motor and vocal tics; however, the current diagnosis of TS patients is subjective, as it is mainly assessed based on the parents' description alongside specific evaluations. The early and accurate diagnosis of TS based on its potential symptoms in children would be of benefit in their future therapy, but reliable diagnoses are difficult due to the lack of objective knowledge of the etiology and pathogenesis of TS. In this study, resting-state electroencephalograms were first collected from 36 patients and 21 healthy controls (HCs); the corresponding resting-state functional networks were then constructed, and the potential differences in network topology between the two groups were extracted by using the topology of the spatial pattern of the network (SPN). Compared to the HCs, the TS patients exhibited decreased frontotemporal/occipital/parietal connectivity. When classifying the two groups, compared to the network properties, the derived SPN features achieved a much higher accuracy of 92.31%. The intrinsic long-range connectivity between the frontal and the temporal/occipital/parietal lobes was damaged in the patient group, and this dysfunctional network pattern might serve as a reliable biomarker to differentiate TS patients from HCs as well as to assess the severity of tic symptoms.

Diagnosis of major depressive disorder using whole-brain effective connectivity networks derived from resting-state functional MRI

OBJECTIVE

It is important to improve identification accuracy for possible early intervention of major depressive disorder (MDD). Recently, effective connectivity (EC), defined as the directed influence of spatially distant brain regions on each other, has been used to find the dysfunctional organization of brain networks in MDD. However, little is known about the ability of whole-brain resting-state EC features in identification of MDD. Here, we employed EC by whole-brain analysis to perform MDD diagnosis.

APPROACH

In this study, we proposed a high-order EC network capturing high-level relationship among multiple brain regions to discriminate 57 patients with MDD from 60 normal controls (NC). In high-order EC networks and traditional low-order EC networks, we utilized the network properties and connection strength for classification. Meanwhile, the support vector machine (SVM) was employed for model training. Generalization of the results was supported by 10-fold cross-validation.

MAIN RESULTS

The classification results showed that the high-order EC network performed better than the low-order EC network in diagnosing MDD, and the integration of these two networks yielded the best classification precision with 95% accuracy, 98.83% sensitivity, and 91% specificity. Furthermore, we found that the abnormal connections of high-order EC in MDD patients involved multiple widely concerned functional subnets, particularly the default mode network and the cerebellar network.

SIGNIFICANCE

The current study indicates whole-brain EC networks, measured by our high-order method, may be promising biomarkers for clinical diagnosis of MDD, and the complementary between high-order and low-order EC will better guide patients to get early interventions as well as treatments.

Depression-like behaviors induced by defective PTPRT activity through dysregulated synaptic functions and neurogenesis

PTPRT has been known to regulate synaptic formation and dendritic arborization of hippocampal neurons. PTPRT-/- null and PTPRT-D401A mutant mice displayed enhanced depression-like behaviors compared with wild-type mice. Transient knockdown of PTPRT in the dentate gyrus enhanced the depression-like behaviors of wild-type mice, whereas rescued expression of PTPRT ameliorated the behaviors of PTPRT-null mice. Chronic stress exposure reduced expression of PTPRT in the hippocampus of mice. In PTPRT-deficient mice the expression of GluR2 (also known as GRIA2) was attenuated as a consequence of dysregulated tyrosine phosphorylation, and the long-term potentiation at perforant-dentate gyrus synapses was augmented. The inhibitory synaptic transmission of the dentate gyrus and hippocampal GABA concentration were reduced in PTPRT-deficient mice. In addition, the hippocampal expression of GABA transporter GAT3 (also known as SLC6A11) was decreased, and its tyrosine phosphorylation was increased in PTPRT-deficient mice. PTPRT-deficient mice displayed reduced numbers and neurite length of newborn granule cells in the dentate gyrus and had attenuated neurogenic ability of embryonic hippocampal neural stem cells. In conclusion, our findings show that the physiological roles of PTPRT in hippocampal neurogenesis, as well as synaptic functions, are involved in the pathogenesis of depressive disorder.

More stringent criteria are needed for diagnosing internet gaming disorder: Evidence from regional brain features and whole-brain functional connectivity multivariate pattern analyses

BACKGROUND

Internet gaming disorder (IGD) is included in the DSM-5 as a provisional diagnosis. Whether IGD should be regarded as a disorder and, if so, how it should be defined and thresholded have generated considerable debate.

METHODS

In the current study, machine learning was used, based on regional and interregional brain features. Resting-state data from 374 subjects (including 148 IGD subjects with DSM-5 scores ≥5 and 93 IGD subjects with DSM-5 scores ≥6) were collected, and multivariate pattern analysis (MVPA) was employed to classify IGD from recreational game use (RGU) subjects based on regional brain features (ReHo) and communication between brain regions (functional connectivity; FC). Permutation tests were used to assess classifier performance.

RESULTS

The results demonstrated that when using DSM-5 scores ≥5 as the inclusion criteria for IGD subjects, MVPA could not differentiate IGD subjects from RGU, whether based on ReHo or FC features or by using different templates. MVPA could differentiate IGD subjects from RGU better than expected by chance when using DSM-5 scores ≥6 with both ReHo and FC features. The brain regions involved in the default mode network and executive control network and the cerebellum exhibited high discriminative power during classification.

DISCUSSION

The current findings challenge the current IGD diagnostic criteria thresholding proposed in the DSM-5, suggesting that more stringent criteria may be needed for diagnosing IGD. The findings suggest that brain regions involved in the default mode network and executive control network relate importantly to the core criteria for IGD.

More stringent criteria are needed for diagnosing internet gaming disorder: Evidence from regional brain features and whole-brain functional connectivity multivariate pattern analyses

BACKGROUND

Internet gaming disorder (IGD) is included in the DSM-5 as a provisional diagnosis. Whether IGD should be regarded as a disorder and, if so, how it should be defined and thresholded have generated considerable debate.

METHODS

In the current study, machine learning was used, based on regional and interregional brain features. Resting-state data from 374 subjects (including 148 IGD subjects with DSM-5 scores ≥5 and 93 IGD subjects with DSM-5 scores ≥6) were collected, and multivariate pattern analysis (MVPA) was employed to classify IGD from recreational game use (RGU) subjects based on regional brain features (ReHo) and communication between brain regions (functional connectivity; FC). Permutation tests were used to assess classifier performance.

RESULTS

The results demonstrated that when using DSM-5 scores ≥5 as the inclusion criteria for IGD subjects, MVPA could not differentiate IGD subjects from RGU, whether based on ReHo or FC features or by using different templates. MVPA could differentiate IGD subjects from RGU better than expected by chance when using DSM-5 scores ≥6 with both ReHo and FC features. The brain regions involved in the default mode network and executive control network and the cerebellum exhibited high discriminative power during classification.

DISCUSSION

The current findings challenge the current IGD diagnostic criteria thresholding proposed in the DSM-5, suggesting that more stringent criteria may be needed for diagnosing IGD. The findings suggest that brain regions involved in the default mode network and executive control network relate importantly to the core criteria for IGD.

Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

BACKGROUND

Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.

METHODS

In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.

RESULTS

After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5-95.3%), sensitivity of 86.4% (95%CI: 64.0-96.4%), and specificity of 88.9% (95%CI: 63.9-98.0%) in the test data (p = 0.0022).

CONCLUSIONS

A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

Automated classification of depression from structural brain measures across two independent community-based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case-control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically-ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well-phenotyped community-based group of current MDD cases with clinical interview-based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, 'STRADL'). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types-SVM, penalised logistic regression or decision tree-either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population-based sample with self-reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses-remitted MDD in STRADL, and lifetime-experienced MDD in UK Biobank. The highest cross-validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self-reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime-experienced MDD (52.68-60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.

The Dynamics of Functional Brain Networks Associated With Depressive Symptoms in a Nonclinical Sample

Brain function depends on the flexible and dynamic coordination of functional subsystems within distributed neural networks operating on multiple scales. Recent progress has been made in the characterization of functional connectivity (FC) at the whole-brain scale from a dynamic, rather than static, perspective, but its validity for cognitive sciences remains under debate. Here, we analyzed brain activity recorded with functional Magnetic Resonance Imaging from 71 healthy participants evaluated for depressive symptoms after a relationship breakup based on the conventional Major Depression Inventory (MDI). We compared both static and dynamic FC patterns between participants reporting high and low depressive symptoms. Between-group differences in static FC were estimated using a standard pipeline for network-based statistic (NBS). Additionally, FC was analyzed from a dynamic perspective by characterizing the occupancy, lifetime, and transition profiles of recurrent FC patterns. Recurrent FC patterns were defined by clustering the BOLD phase-locking patterns obtained using leading eigenvector dynamics analysis (LEiDA). NBS analysis revealed a brain subsystem exhibiting significantly lower within-subsystem correlation values in more depressed participants (high MDI). This subsystem predominantly comprised connections between regions of the default mode network (i.e., precuneus) and regions outside this network. On the other hand, LEiDA results showed that high MDI participants engaged more in a state connecting regions of the default mode, memory retrieval, and frontoparietal network (p-FDR = 0.012); and less in a state connecting mostly the visual and dorsal attention systems (p-FDR = 0.004). Although both our analyses on static and dynamic FC implicate the role of the precuneus in depressive symptoms, only including the temporal evolution of BOLD FC helped to disentangle over time the distinct configurations in which this region plays a role. This finding further indicates that a holistic understanding of brain function can only be gleaned if the temporal dynamics of FC is included.

Functional neural network configuration in late childhood varies by age and cognitive state

•

fMRI data from 60 children aged 9–12 during resting and tasks involving decision making, visual perception, and working memory were examined.

•

At rest, the child brain exhibited network organization similar to adults though the degree of similarity was age- and network-dependent.

•

During tasks, brain network configurations showed task-induced and age-related changes in integration.

•

Frontoparietal network showed flexible connectivity pattern across states while networks for sensory and motor processing remained stable.

•

Findings demonstrate that network connectivity characteristics may serve as markers for neural and cognitive maturation.

Late childhood and early adolescence is characterized by substantial brain maturation which contributes to both adult-like and age-dependent resting-state network connectivity patterns. However, it remains unclear whether these functional network characteristics in children are subject to differential modulation by distinct cognitive demands as previously found in adults. We conducted network analyses on fMRI data from 60 children (aged 9–12) during resting and during three distinct tasks involving decision making, visual perception, and spatial working memory. Graph measures of network architecture, functional integration, and flexibility were calculated for each of the four states. During resting state, the children’s network architecture was similar to that in young adults (N = 60, aged 20–23) but the degree of similarity was age- and network-dependent. During the task states, the children's whole-brain network exhibited enhanced integration in response to increased cognitive demand. Additionally, the frontoparietal network showed flexibility in connectivity patterns across states while networks implicated in motor and visual processing remained relatively stable. Exploratory analyses suggest different relationships between behavioral performance and connectivity profiles for the working memory and perceptual tasks. Together, our findings demonstrate state- and age-dependent features in functional network connectivity during late childhood, potentially providing markers for brain and cognitive development.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

Alleviation of Depression by Glucagon-Like Peptide 1 Through the Regulation of Neuroinflammation, Neurotransmitters, Neurogenesis, and Synaptic Function

Depression has emerged as a major cause of mortality globally. Many studies have reported risk factors and mechanisms associated with depression, but it is as yet unclear how these findings can be applied to the treatment and prevention of this disorder. The onset and recurrence of depression have been linked to diverse metabolic factors, including hyperglycemia, dyslipidemia, and insulin resistance. Recent studies have suggested that depression is accompanied by memory loss as well as depressive mood. Thus, many researchers have highlighted the relationship between depressive behavior and metabolic alterations from various perspectives. Glucagon-like peptide-1 (GLP-1), which is secreted from gut cells and hindbrain areas, has been studied in metabolic diseases such as obesity and diabetes, and was shown to control glucose metabolism and insulin resistance. Recently, GLP-1 was highlighted as a regulator of diverse pathways, but its potential as the therapeutic target of depressive disorder was not described comprehensively. Therefore, in this review, we focused on the potential of GLP-1 modulation in depression.

Connectome-based models can predict early symptom improvement in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a debilitating mental illness with more than 50% of patients not achieving an adequate response using first-line treatments. Reliable models that predict antidepressant treatment outcome are needed to guide clinical decision making. We aimed to build predictive models of treatment improvement for MDD patients using machine learning approaches based on fMRI resting-state functional connectivity patterns.

METHODS

Resting-state fMRI data were acquired from 192 untreated MDD patients at recruitment, and their severity of depression was assessed by Hamilton Rating Scale for Depression (HAMD) at baseline. Patients were given medication after the initial MR scan and their symptoms were monitored through HAMD for a period of six months. Connectome-based predictive modeling (CPM) algorithms were implemented to predict the improvement in HAMD score at one month from resting-state connectivity at baseline. Additionally, by selectively combining the features from all leave-one-out iterations in the model building stage, we created a consensus model that could be generalized to predict improvement in HAMD score in samples of non-overlapping subjects at different time points.

RESULTS

Using baseline functional connectivity, CPM successfully predicted symptom improvement of depression at one month. In addition, a consensus 'MDD improvement model' could predict symptom improvement for novel individuals at the two-week, one-month, two-month and three-month time points after antidepressant treatment.

CONCLUSIONS

Individual pre-treatment functional brain networks contain meaningful information that can be gleaned to build predictors of treatment outcome. The identified MDD improvement networks could be an appropriate biomarker for predicting individual therapeutic response of antidepressant treatment. Replication and validation using other large datasets will be a key next step before these models can be used in clinical practice.

Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

Altered neurometabolism in major depressive disorder: A whole brain 1H-magnetic resonance spectroscopic imaging study at 3T

INTRODUCTION

Major depressive disorder (MDD) is a severe mental disorder with a neurobiological basis that is poorly understood. Several studies demonstrated widespread, functional and neurometabolic alterations in MDD. However, little is known about whole brain neurometabolic alterations in MDD.

METHOD

Thirty-two patients with MDD and 32 paired on a one-to-one basis healthy controls (CTRL) underwent ¹H-whole brain spectroscopic (¹H-WBS) imaging. Lobar and cerebellar metabolite concentrations of brain N-acetylaspartate (NAA), total choline (tCho), total creatine (tCr), glutamine (Gln), glutamate (Glu), and myo-Inositol (mI) were assessed in patients and controls.

RESULTS

Decreased NAA, tCho, and tCr were found in the right frontal and right parietal lobe in MDD compared to CTRL, and to a lesser extent in the left frontal lobe. Furthermore, in MDD increased glutamine was observed in the right frontal lobe and bitemporal lobes, and increased glutamate in the cerebellum.

CONCLUSION

Altered global neurometabolism examined using ¹H-WBS imaging in MDD may be interpreted as signs of neuronal dysfunction, altered energy metabolism, and oligodendrocyte dysfunction. In particular, the parallel decrease in NAA, tCr and tCho in the same brain regions may be indicative of neuronal dysfunction that may be counterbalanced by an increase of the neuroprotective metabolite glutamine. Future prospective investigations are warranted to study the functional importance of these findings.

Transition and Dynamic Reconfiguration of Whole-Brain Network in Major Depressive Disorder

Major depressive disorder (MDD) has been characterized by abnormal brain activity and interactions across the whole-brain functional networks. However, the underlying alteration of brain dynamics remains unclear. Here, we aim to investigate in detail the temporal dynamics of brain activity for MDD, and to characterize the spatiotemporal specificity of whole-brain networks and transitions across them. We developed a hidden Markov model (HMM) analysis for resting-state functional magnetic resonance imaging (fMRI) from two independent cohorts with MDD. In particular, one cohort included 127 MDD patients and 117 gender- and age-matched healthy controls, and the other included 44 MDD patients and 33 controls. We identified brain states characterized by the engagement of distinct functional networks that recurred over time and assessed the dynamical configuration of whole-brain networks and the patterns of activation of states that characterized the MDD groups. Furthermore, we analyzed the community structure of transitions across states to investigate the specificity and abnormality of transitions for MDD. Based on our identification of 12 HMM states, we found that the temporal reconfiguration of states in MDD was associated with the high-order cognition network (DMN), subcortical network (SUB), and sensory and motor networks (SMN). Further, we found that the specific module of transitions was closely related to MDD, which were characterized by two HMM states with opposite activations in DMN, SMN, and subcortical areas. Notably, our results provide novel insights into the dynamical circuit configuration of whole-brain networks for MDD and suggest that brain dynamics should remain a prime target for further MDD research.

Resting-state global EEG connectivity predicts depression and anxiety severity

There is a recent interest in finding neurophysiological biomarkers which will facilitate the diagnosis and understanding of the neural basis of different psychiatric disorders. In this paper, we evaluated the resting-state global EEG connectivity as a potential biomarker for depressive and anxiety symptoms. For this, we evaluated a population of 119 subjects, including 75 healthy subjects and 44 patients with major depressive disorder. We calculated the global connectivity (spectral coherence) in a setup of 60 EEG channels, for six different spectral bands: theta, alpha1, alpha2, beta1, beta2, and gamma. These global connectivity scores were used to train a Support Vector Regressor to predict symptoms measured by the Beck Depression Inventory (BDI) and the Spielberger Trait Anxiety Inventory (TAI). Experiments showed a significant prediction of both symptoms, with a mean absolute error (MAE) of 8.07±6.98 and 11.52±8.7 points, respectively. Among the most discriminating features, the global connectivity in the alpha2 band (10.0-12.0Hz) presented significantly positive Spearman's correlation with the depressive (rho = 0.32, p_FDR <0.01), and the anxiety symptoms (rho = 0.26, p_FDR<0.01).Clinical relevance-This study demonstrates that EEG global connectivity can be used to predict depression and anxiety symptoms measured by widely used questionnaires.

Identifying psychosis spectrum youth using support vector machines and cerebral blood perfusion as measured by arterial spin labeled fMRI

•

Psychosis spectrum (PS) youth can be identified with support vector machines.

•

Classification is improved when youth with psychiatric comorbidities are excluded.

•

Cerebral blood flow (CBF) connectivity differences were noted between PS and non-PS.

Altered cerebral blood flow (CBF), as measured by arterial spin labelling (ASL), has been observed in several psychiatric conditions, but is a generally underutilized MRI technique, especially in the study of psychosis spectrum (PS) symptoms. We aimed to determine group differences in ASL resting state functional connectivity (rsFC) between PS and non-PS youth, and the reliability of a support vector machine (SVM) classifier trained on ASL rsFC features to differentiate PS and non-PS youth, especially compared to blood oxygen level dependent (BOLD) fMRI. 1146 youth aged 8–22 with ASL and BOLD data from the Philadelphia Neurodevelopmental Cohort were analyzed. Widespread ASL hyperconnectivity was found in the left cuneus, precuneus, and dorsolateral prefrontal cortex, and hypoconnectivity was found in the left cingulate cortex and orbitofrontal area (multiple linear regression, FDR corrected). An SVM trained on ASL and BOLD features outperformed either modality alone (AUC_BOTH = 0.72 versus AUC_ASL = 0.68 and AUC_BOLD = 0.67). Classification performance and precision improved when the non-PS group had no psychiatric comorbidities. The relative success of the classifier suggests ASL rsFC changes exist in PS individuals that differ from BOLD rsFC changes, and extends previous findings of CBF dysregulation in PS.

A toolbox for brain network construction and classification (BrainNetClass)

Brain functional network has been increasingly used in understanding brain functions and diseases. While many network construction methods have been proposed, the progress in the field still largely relies on static pairwise Pearson's correlation-based functional network and group-level comparisons. We introduce a "Brain Network Construction and Classification (BrainNetClass)" toolbox to promote more advanced brain network construction methods to the filed, including some state-of-the-art methods that were recently developed to capture complex and high-order interactions among brain regions. The toolbox also integrates a well-accepted and rigorous classification framework based on brain connectome features toward individualized disease diagnosis in a hope that the advanced network modeling could boost the subsequent classification. BrainNetClass is a MATLAB-based, open-source, cross-platform toolbox with both graphical user-friendly interfaces and a command line mode targeting cognitive neuroscientists and clinicians for promoting reliability, reproducibility, and interpretability of connectome-based, computer-aided diagnosis. It generates abundant classification-related results from network presentations to contributing features that have been largely ignored by most studies to grant users the ability of evaluating the disease diagnostic model and its robustness and generalizability. We demonstrate the effectiveness of the toolbox on real resting-state functional MRI datasets. BrainNetClass (v1.0) is available at https://github.com/zzstefan/BrainNetClass.

Support Vector Machines and Affective Science

Support vector machines (SVMs) are being used increasingly in affective science as a data-driven classification method and feature reduction technique. Whereas traditional statistical methods typically compare group averages on selected variables, SVMs use a predictive algorithm to learn multivariate patterns that optimally discriminate between groups. In this review, we provide a framework for understanding the methods of SVM-based analyses and summarize the findings of seminal studies that use SVMs for classification or data reduction in the behavioral and neural study of emotion and affective disorders. We conclude by discussing promising directions and potential applications of SVMs in future research in affective science.

Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder

OBJECTIVE

Major depressive disorder (MDD) is a ubiquitous mental illness with heterogeneous symptoms, however, the pathophysiology mechanisms are still not fully understood. Clinical and preclinical studies suggested that depression could cause disturbances in sensory perception systems, disruptions in auditory and visual functions may serve as an essential clinical features underlying MDD.

METHODS

The current study investigated the abnormal intrinsic connectivity within and between visual and auditory networks in 95 MDD patients and 97 age-, gender-, education level-matched healthy controls (HCs) by using resting-state functional magnetic resonance imaging (fMRI). One auditory network (AN) and three visual components including visual component 1 (VC1), VC2, and VC3 were identified by using independent component analysis method based on the fMRI networks during the resting state with the largest spatial correlations, combining with brain regions and specific network templates.

RESULTS

We found that MDD could be characterized by the following disrupted network model relative to HCs: (i) reduced within-network connectivity in the AN, VC2, and VC3; (ii) reduced between-network connectivity between the AN and the VC3. Furthermore, aberrant functional connectivity (FC) within the visual network was linked to the clinical symptoms.

CONCLUSIONS

Overall, our results demonstrated that abnormalities of FC in perception systems including intrinsic visual and auditory networks may explain neurobiological mechanisms underlying MDD and could serve as a potential effective biomarker.

Altered functional connectivity of the posterior cingulate cortex in type 2 diabetes with cognitive impairment

The posterior cingulate cortex (PCC) has been suggested to be a cortical hub of the default mode network (DMN). Our goal in the current study was to determine whether there were alterations in the PCC's functional connectivity (FC) with whole brain regions in type 2 diabetes mellitus (T2DM) and to determine their relationships with cognitive dysfunction. In this study, the FC of the PCC was characterized by using resting-state functional MRI and a seed-based whole-brain correlation method in 24 T2DM patients and compared with 24 well-matched healthy controls. Spearman correlation analysis was performed to determine the relationships between the FC of the PCC and cognitive dysfunction. T2DM was associated with a significantly decreased FC of the PCC to widespread brain regions (p < 0.05, corrected for AlphaSim). We also found that the FC of the PCC in these brain regions was positively correlated with several neuropsychological test scores, such as the FC to the right angular gyrus (AnG) and the bilateral middle temporal gyrus (MTG) with the Auditory Verbal Learning Test (AVLT) and the FC to the bilateral inferior frontal gyrus (IFG) with the digit span test (DST). Moreover, the FCs of the PCC to the right superior parietal lobule (SPL), bilateral temporal lobes and left cerebrum were detected as negatively correlated with the Trail Making Test (TMT). No such correlations were detected in healthy controls. The present study provides useful information about the effect of the FC of the PCC on the underlying neuropathological process of T2DM-related cognitive dysfunction and may provide supporting evidence for further molecular biology studies.

Abnormal medial prefrontal cortex functional connectivity and its association with clinical symptoms in chronic low back pain

Accumulating evidence has shown that complicated brain systems are involved in the development and maintenance of chronic low back pain (cLBP), but the association between brain functional changes and clinical outcomes remains unclear. Here, we used resting-state functional magnetic resonance imaging (fMRI) and multivariate pattern analysis to identify abnormal functional connectivity (FC) between the default mode, sensorimotor, salience, and central executive brain networks in cLBP and tested whether abnormal FCs are related to pain and comorbid symptoms. Fifty cLBP patients and 44 matched healthy controls (HCs) underwent an fMRI scan, from which brain networks were identified by independent component analysis. Multivariate pattern analysis, graph theory approaches, and correlation analyses were applied to find abnormal FCs that were associated with clinical symptoms. Findings were validated on a second cohort of 30 cLBP patients and 30 matched HCs. Results showed that the medial prefrontal cortex/rostral anterior cingulate cortex had abnormal FCs with brain regions within the default mode network and with other brain networks in cLBP patients. These altered FCs were also correlated with pain duration, pain severity, and pain interference. Finally, we found that resting-state FC could discriminate cLBP patients from HCs with 91% accuracy in the first cohort and 78% accuracy in the validation cohort. Our findings suggest that the medial prefrontal cortex/rostral anterior cingulate cortex may be an important hub for linking the default mode network with the other 3 networks in cLBP patients. Elucidating the altered FCs and their association with clinical outcomes will enhance our understanding of the pathophysiology of cLBP and may facilitate the development of pain management approaches.

Consciousness and the Dimensionality of DOC Patients via the Generalized Ising Model

The data from patients with severe brain injuries show complex brain functions. Due to the difficulties associated with these complex data, computational modeling is an especially useful tool to examine the structure-function relationship in these populations. By using computational modeling for patients with a disorder of consciousness (DoC), not only we can understand the changes of information transfer, but we also can test changes to different states of consciousness by hypothetically changing the anatomical structure. The generalized Ising model (GIM), which specializes in using structural connectivity to simulate functional connectivity, has been proven to effectively capture the relationship between anatomical structures and the spontaneous fluctuations of healthy controls (HCs). In the present study we implemented the GIM in 25 HCs as well as in 13 DoC patients diagnosed at three different states of consciousness. Simulated data were analyzed and the criticality and dimensionality were calculated for both groups; together, those values capture the level of information transfer in the brain. Ratifying previous studies, criticality was observed in simulations of HCs. We were also able to observe criticality for DoC patients, concluding that the GIM is generalizable for DoC patients. Furthermore, dimensionality increased for the DoC group as compared to healthy controls, and could distinguish different diagnostic groups of DoC patients.

Multimodal Abnormalities of Brain Structure and Function in Major Depressive Disorder: A Meta-Analysis of Neuroimaging Studies

OBJECTIVE

Imaging studies of major depressive disorder have reported structural and functional abnormalities in a variety of spatially diverse brain regions. Quantitative meta-analyses of this literature, however, have failed to find statistically significant between-study spatial convergence, other than transdiagnostic-only effects. In the present study, the authors applied a novel multimodal meta-analytic approach to test the hypothesis that major depression exhibits spatially convergent structural and functional brain abnormalities.

METHODS

This coordinate-based meta-analysis included voxel-based morphometry (VBM) studies and resting-state voxel-based pathophysiology (VBP) studies of blood flow, glucose metabolism, regional homogeneity, and amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF). Input data were grouped into three primary meta-analytic classes: gray matter atrophy, increased function, and decreased function in patients with major depression relative to healthy control subjects. In secondary meta-analyses, the data were grouped across primary categories, and in tertiary analyses, by medication status and absence of psychiatric comorbidity. Activation likelihood estimation was used for all analyses.

RESULTS

A total of 92 publications reporting 152 experiments were identified, collectively representing 2,928 patients with major depressive disorder. The primary analyses detected no convergence across studies. The secondary analyses identified portions of the subgenual cingulate cortex, hippocampus, amygdala, and putamen as demonstrating convergent abnormalities. The tertiary analyses (clinical subtypes) showed improved convergence relative to the secondary analyses.

CONCLUSIONS

Coordinate-based meta-analysis identified spatially convergent structural (VBM) and functional (VBP) abnormalities in major depression. The findings suggest replicable neuroimaging features associated with major depression, beyond the transdiagnostic effects reported in previous meta-analyses, and support a continued research focus on the subgenual cingulate and other selected regions' role in depression.