Graphical abstract

Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.

ObjectiveTo investigate the dynamic functional connectivity of thalamocortical networks in interictal migraine patients and whether clinical features are associated with abnormal connectivity.MethodsWe investigated dynamic functional network connectivity (dFNC) of the migraine brain in 89 interictal migraine patients and 70 healthy controls. We focused on the temporal properties of thalamocortical connectivity using sliding window cross-correlation, clustering state analysis, and graph-theory methods. Relationships between clinical symptoms and abnormal dFNC were evaluated using a multivariate linear regression model.ResultsFive dFNC brain states were identified to characterize and compare dynamic functional connectivity patterns. We demonstrated that migraineurs spent more time in a strongly interconnected between-network state, but they spent less time in a sparsely connected state. Interestingly, we found that abnormal posterior thalamus (pulvinar nucleus) dFNC with the visual cortex and the precuneus were significantly correlated with headache frequency of migraine. Further topologic measures revealed that migraineurs had significantly lower efficiency of information transfer in both global and local dFNC.ConclusionOur results demonstrated a transient pathologic state with atypical thalamocortical connectivity in migraineurs and extended current findings regarding abnormal thalamocortical networks and dysrhythmia in migraine.

The human brain is a highly dynamic system with non-stationary neural activity and rapidly-changing neural interaction. Resting-state dynamic functional connectivity (dFC) has been widely studied during recent years, and the emerging aberrant dFC patterns have been identified as important features of many mental disorders such as schizophrenia (SZ). However, only focusing on the time-varying patterns in FC is not enough, since the local neural activity itself (in contrast to the inter-connectivity) is also found to be highly fluctuating from research using high-temporal-resolution imaging techniques. Exploring the time-varying patterns in brain activity and their relationships with time-varying brain connectivity is important for advancing our understanding of the co-evolutionary property of brain network and the underlying mechanism of brain dynamics. In this study, we introduced a framework for characterizing time-varying brain activity and exploring its associations with time-varying brain connectivity, and applied this framework to a resting-state fMRI dataset including 151 SZ patients and 163 age- and gender matched healthy controls (HCs). In this framework, 48 brain regions were first identified as intrinsic connectivity networks (ICNs) using group independent component analysis (GICA). A sliding window approach was then adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dFC, which were used to measure time-varying brain activity and time-varying brain connectivity respectively. The dALFF was further clustered into six reoccurring states by the k-means clustering method and the group difference in occurrences of dALFF states was explored. Lastly, correlation coefficients between dALFF and dFC were calculated and the group difference in these dALFF-dFC correlations was explored. Our results suggested that 1) ALFF of brain regions was highly fluctuating during the resting-state and such dynamic patterns are altered in SZ, 2) dALFF and dFC were correlated in time and their correlations are altered in SZ. The overall results support and expand prior work on abnormalities of brain activity, static FC (sFC) and dFC in SZ, and provide new evidence on aberrant time-varying brain activity and its associations with brain connectivity in SZ, which might underscore the disrupted brain cognitive functions in this mental disorder.

Subcortical ischemic vascular disease (SIVD) is a major subtype of vascular dementia with features that overlap clinically with Alzheimer's disease (AD), confounding diagnosis.Neuroimaging is a more specific and biologically based approach for detecting brain changes and thus may help to distinguish these diseases. There is still a lack of knowledge regarding the shared and specific functional brain abnormalities, especially functional connectivity changes in relation to AD and SIVD. In this study, we investigated both static functional network connectivity (sFNC) and dynamic FNC (dFNC) between 54 intrinsic connectivity networks in 19 AD patients, 19 SIVD patients, and 38 agematched healthy controls. The results show that both patient groups have increased sFNC between the visual and cerebellar (CB) domains but decreased sFNC between the cognitive-control and CB domains. SIVD has specifically decreased sFNC within the sensorimotor domain while AD has specifically altered sFNC between the default-mode and CB domains. In addition, SIVD has more occurrences and a longer dwell time in the weakly connected dFNC states, but with fewer occurrences and a shorter dwell time in the strongly connected dFNC states. AD has both similar and opposite changes in certain dynamic features. More importantly, the dynamic features are found to be associated with cognitive performance. Our findings highlight similar and distinct functional connectivity alterations in AD and SIVD from both static and dynamic perspectives and indicate dFNC to be a more important biomarker for dementia since its progressively altered patterns can better track cognitive impairment in AD and SIVD. K E Y W O R D S Alzheimer's disease (AD), cognitive impairment, dynamic functional network connectivity (dFNC), resting-state functional network connectivity (rs-FNC), subcortical ischemic vascular disease (SIVD)

ObjectiveTo identify and validate an fMRI-based neural marker for migraine without aura (MwoA) and to examine its association with treatment response.MethodsWe conducted cross-sectional studies with resting-state fMRI data from 230 participants and machine learning analyses. In studies 1 through 3, we identified, cross-validated, independently validated, and cross-sectionally validated an fMRI-based neural marker for MwoA. In study 4, we assessed the relationship between the neural marker and treatment responses in migraineurs who received a 4-week real or sham acupuncture treatment, or were waitlisted, in a registered clinical trial.ResultsIn study 1 (n = 116), we identified a neural marker with abnormal functional connectivity within the visual, default mode, sensorimotor, and frontal-parietal networks that could discriminate migraineurs from healthy controls (HCs) with 93% sensitivity and 89% specificity. In study 2 (n = 38), we investigated the generalizability of the marker by applying it to an independent cohort of migraineurs and HCs and achieved 84% sensitivity and specificity. In study 3 (n = 76), we verified the specificity of the marker with new datasets of migraineurs and patients with other chronic pain disorders (chronic low back pain and fibromyalgia) and demonstrated 78% sensitivity and 76% specificity for discriminating migraineurs from nonmigraineurs. In study 4 (n = 116), we found that the changes in the marker responses showed significant correlation with the changes in headache frequency in response to real acupuncture.ConclusionWe identified an fMRI-based neural marker that captures distinct characteristics of MwoA and can link disease pattern changes to brain changes.

Individuals at clinical high-risk (CHR) for psychosis are characterized by attenuated psychotic symptoms. Only a minority of CHR individuals convert to full-blown psychosis. Therefore, there is a strong interest in identifying neurobiological abnormalities underlying the psychosis risk syndrome. Dynamic functional connectivity (DFC) captures time-varying connectivity over short time scales, and has the potential to reveal complex brain functional organization. Based on resting-state functional magnetic resonance imaging (fMRI) data from 70 healthy controls (HCs), 53 CHR individuals, and 58 early illness schizophrenia (ESZ) patients, we applied a novel group information guided ICA (GIG-ICA) to estimate inherent connectivity states from DFC, and then investigated group differences. We found that ESZ patients showed more aberrant connectivities and greater alterations than CHR individuals. Results also suggested that disease-related connectivity states occurred in CHR and ESZ groups. Regarding the dominant state with the highest contribution to dynamic connectivity, ESZ patients exhibited greater impairments than CHR individuals primarily in the cerebellum, frontal cortex, thalamus and temporal cortex, while CHR and ESZ populations shared common aberrances mainly in the supplementary motor area, parahippocampal gyrus and postcentral cortex. CHR-specific changes were also found in the connections between the superior frontal gyrus and calcarine cortex in the dominant state. Our findings suggest that CHR individuals generally show an intermediate functional connectivity pattern between HCs and SZ patients but also have unique connectivity alterations.

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