Keywords: Task/Intervention Based fMRI, fMRI (task based)

Motivation: Developmental Dyslexia (DD) is a complex and heritable neurodevelopmental disorder with heterogeneous genotype-phenotype pathways.

Goal(s): Utilise fMRI as a bridge between genetic factors (DD-candidate risk genes) and behavioral traits (proficiency in reading skills).

Approach: A GLM was used to test for relationships between reading proficiency, genetic mutation, and neural activations of two visual-attentive tasks.

Results: A genetic vulnerability to alterations in neural activation was found in the ventral attentive and salient networks during reading-related stimuli in subjects with poor reading proficiency.

Impact: Functional MRI has shown to be a valuable mediator linking genotype to phenotype, possibly leading to the optimization of criteria to diagnose Developmental Dyslexia and the early identification of children with a genetically driven susceptibility.

Keywords: Functional Connectivity, Brain Connectivity, Dyslexia, Dynamic Causal Modeling

Motivation: Alterations in functional connectivity between regions involved in reading and visuo-attention networks have been associated with developmental dyslexia. However, the causal relationship between regional activity remains unknown.

Goal(s): We aimed to investigate the causal relationship between regions of the visuo-attention network in developmental dyslexia and typical readers during a coherent motion detection task.

Approach: Using Dynamic Causal Modeling, the causal connectivity between regions in the cortex and cerebellum was estimated to understand aberrant network function.

Results: Children with developmental dyslexia showed remarkable differences in patterns of excitatory and inhibitory communication between cerebellum and visuo-attention regions compared to typical reader children.

Impact: Dynamic Causal Modeling can evaluate cortico-cerebellar causal relationship (i.e., effective connectivity) in healthy subjects and in neurodevelopmental conditions such as developmental dyslexia. New evidence points toward a critical role of the cerebellum in reading impairment, with potential consequences for intervention.

Keywords: Functional Connectivity, Aging

Motivation: The impact of different  cerebral small vessel disease (CSVD) burden on brain structural and functional connectivity coupling and their correlation with neurocognitive outcomes remain largely unknown.

Goal(s): To explore the alterations of structural and functional connection network (SC-FC) coupling in the whole brain and different functional modules of patients with different CSVD burden compared with healthy controls.

Approach: Diffusion tensor imaging (DTI) and Resting-state blood-oxygen-level-dependent (BOLD) fMRI techniques were used to analyze structural and functional brain connections.

Results: Severe CSVD burden patients exhibited significantly decreased whole-brain SC-FC coupling, reduced modular SC-FC coupling and associated with impairment of cognitive outcomes. 

Impact: SC-FC coupling might provide a more sensitive neuroimaging biomarker of CSVD burden as well as new insights into the pathophysiologic mechanisms of the clinical development of CSVD.

Keywords: Functional Connectivity, fMRI (resting state), Parkinson's disease, Dorsal Raphe Nucleus, Sleep disturbance, correlational tractography

Motivation: The role of the dorsal raphe nucleus (DRN) in sleep related pathologies in Parkinson’s disease (PD) remains under investigated.

Goal(s): To characterize functional connectivity patterns and correlational structural tractography changes specific to the DRN in PD and sleep-related symptoms.

Approach: Resting-state functional MRI and diffusion spectrum MRI metrics were compared across PD patients and healthy controls  experiencing severe sleep disturbances.

Results: We found changes in functional connectivity profiles of the DRN and findings suggesting axonal damage that showed stronger correlation with sleep symptoms in healthy controls compared to patients, implying potentially distinct pathophysiological mechanisms in symptom development.

Impact: Clarifying involvement of the dorsal raphe nucleus and serotonergic pathways in the pathogenesis of sleep symptoms in Parkinson’s Disease may contribute to development of novel therapies targeted toward specific dysfunctional pathways involved in this quality-of-life disturbing condition.

Keywords: fMRI Analysis, fMRI (resting state), ALFF, taVNS

Motivation: A growing body of evidence suggests that taVNS may improve the motor function of PD patients whereas little is known about the neuropathologic mechanism.

Goal(s): To explore the potential mechanism of taVNS in treating PD by rs-fMRI.
 

Approach: Fifty patients with PD underwent three times fMRI scanning. And the difference in ALFF among the baeline state,real taVNS and sham taVNS state were investigated. 

Results: Compared with baseline and sham taVNS state, the ALFF value showed a significant decrease in 5 clusters. Pearson correlation analysis indicated ALFF of SPL_r in real taVNS condition was negatively correlated with the total UPDRS score, UPDRS-Ⅲscore and PDQ.

Impact: The taVNS may produce treatment effects by modulating the abnormal ALFF of sensorimotor network,salience network and visual network. This may shed light on the neural mechanisms underlying taVNS treatment of PD.

Keywords: Task/Intervention Based fMRI, fMRI, Deep Brain Stimulation (DBS); Neuromodulation; Reliability; Reproducibility; Test-retest

Motivation: fMRI studies are advancing DBS biomarker development, but data reproducibility is unclear.

Goal(s): To evaluate resting-state fMRI reliability in 16 patients with DBS for Parkinson’s disease.

Approach: fMRI was repeated for DBS-ON and DBS-OFF conditions. Test-retest connectomes were correlated to measure reproducibility and compared across DBS conditions and clinical parameters. Signal reproducibility around the leads was also explored.

Results: Stimulation reduced reproducibility around the leads and across multiple networks, differing by brain target. Patients with less tremor and/or more rigidity and bradykinesia, and relative lower symptom and brain response to DBS had more reproducible functional connectivity.

Impact: The results enhance our understanding of the reliability of resting-state fMRI derivatives in the presence of DBS leads and during stimulation. Realizing the reliability of these data is critical to clinical translation of fMRI-based biomarkers to improve the DBS strategy.

Keywords: Functional Connectivity, Brain Connectivity, Brain modeling, The Virtual Brain, conduction velocity

Motivation: The Virtual Brain (TVB) is a neuroinformatic platform used to perform brain dynamic simulations integrating subject-specific imaging data. In standard TVB the input conduction velocity is fixed, making it insensitive to local effective measures of myelin content. 

Goal(s): Here we parameterized signal conduction velocity for TVB simulations.

Approach: Considering myelin role in efficient neural conduction, myelin measures were integrated into TVB.

Results: Making TVB sensitive to myelin content highlights variations in simulation outcomes with potential improvements in capturing spatiotemporal dynamics of brain activity. This advancement opens perspectives for realizing more accurate subject-specific simulations, representing a new step towards brain digital twinning. 

Impact: Brain Digital Twin technologies will transform personalized medicine, providing a better understanding of pathophysiological underpinnings of diseases. Our study demonstrates how simulating brain activity with The Virtual Brain model improves when integrating subject-specific neural conduction values, calculated from myelin measures.

Keywords: Functional Connectivity, Brain Connectivity, Schizophrenia, negative symptom, network segregation

Motivation: Empirical findings suggest that negative symptoms of schizophrenia could be improved by cognitive training regime.

Goal(s): This study seeks to (1) understand how brain network architecture supporting task performance may be related to negative symptoms and (2) whether better task performance could be linked to differences in intrinsic functional network architecture.

Approach: Schizophrenia patients underwent resting state and dual-modality N-back task fMRI scans. Network segregation was summarised using the system segregation index for each network.

Results: Functional network segregation during both rest and task was associated with negative symptom severity and task performance.

Impact: The current study highlighted the common ground of altered network segregation between negative symptoms and task performance in schizophrenia and encouraged future study on improving negative symptoms and network communication through cognitive training interventions.

Keywords: fMRI Analysis, fMRI (resting state), Laminar connectivity, depression

Motivation: A previous own study in healthy volunteers indicated that global laminar connectivity is highly dynamic, suggesting that it could be sensitive to altered brain conditions.

Goal(s): Does global laminar connectivity change in depression?

Approach: We acquired high-resolution fMRI data from patients before and after treatment and conducted a dynamic connectivity analysis focused on cortical depth.

Results: The prevalence of the depth-connectivity states co-evolved with the psychometric scores of patients.

Impact: The presented results may motivate other researchers working on laminar fMRI to average across ROIs and evaluate the effect of diverse brain conditions on the global component of depth-dependent connectivity, whose potential relevance is suggested by our preliminary studies.

Keywords: Functional Connectivity, fMRI (resting state), Major depressive disorder; Global signal topography

Motivation: Global signal (GS) distribution changes remain unclear in major depressive disorder (MDD).

Goal(s): This study aimed to explore abnormal GS topography in MDD, and its underlying structural mechanism and relationship with clinical assessments. 

Approach: We used resting-state fMRI and T1-weighted data from the REST-meta-MDD consortium, and calculated the GS  correlation (GSCORR) and gray matter volume (GMV). 

Results: We found decreased GS topography in sensorimotor networks in recurrent MDD, and altered GMV-GSCORR coupling in cingulo-opercular and frontoparietal/occipital networks in first-episode and recurrent MDD, respectively. The alterations of GS topography in temporal lobe and cerebellum correlated with HAMD/HAMA scores, which were partially mediated by GMV.
   

Impact: Our findings demonstrated that first-episode and recurrent MDD showed different alterations in GS topography, which were associated with cortical GMV and clinical symptoms of patients, contributing to the understanding of relationship between global and local neuronal activities in MDD. 

Keywords: fMRI Analysis, fMRI (resting state), MR value; major depressive disorder; subtypying; genetic mechanisms; neurotransmitter; cognition

Motivation: There’s a large clinical heterogeneity presented in MDD and inconsistent MRI evidence on abnormal functional connectivity (FC) in MDD, let alone the unclear biological mechanisms underlying the neuroimaging alterations.

Goal(s): To identify FC based subtypes of MDD and their genetic mechanisms and neurotransmission patterns.

Approach: Consensus clustering of FC was applied to subtyping MDD. Correlation analyses were used to explore the underlying biological mechanisms of FC alterations in each subtype.

Results: Two stable neurophysiological MDD subtypes were found. While the two subtypes were indistinguishable by clinical symptoms, FC alterations of each subtype had distinct spatial correlations with cognition, gene, and neurotransmission profiles.

Impact: Our findings suggested the presence of two neuroimaging subtypes in MDD and the two subtypes can be characterized by different genetic mechanisms, neurotransmitter receptor/transporter profiles, and cognition types, providing new clues to understand the pathophysiology of MDD.

Keywords: Functional Connectivity, Brain, Migraine, Longitudinal, Multilevel Clinical Connectome Fingerprinting

Motivation: Case-control fMRI studies spanning the entire migraine cycle are lacking, precluding a complete assessment of brain functional connectivity in migraine. Such studies are essential for understanding the inherent changes in the brain of migraine patients as well as transient changes along the cycle.

Goal(s): Our goal was to determine the influence of the migraine cycle on individual functional connectome fingerprints.

Approach: Functional connectivity (FC) was longitudinally studied for migraine patients (across the four different cycle phases) and matched healthy controls.

Results: We observed greater heterogeneity in FC patterns of migraine patients and significant changes in FC across the cycle compared to controls.

Impact: This work represents the first case-control fMRI longitudinal study across the whole migraine cycle. Building upon clinical connectome fingerprinting, applied for the first time to migraine, it tackles a major cause of disability worldwide, contributing to developing connectome-based disease biomarkers.

Keywords: fMRI Analysis, fMRI (resting state), Language Function

Motivation: Automated detection of resting-state language network with independent components analysis (ICA) of brain tumor patients is challenging.

Goal(s): Develop an algorithm to detect the language network with ICA guided by a probabilistic overlap map (POM).

Approach: POM was generated from sentence completion presurgical fMRI of 283 patients. Probabilistic template matching performs a direct search over probability thresholds and component numbers. Independent dataset of 28 patients was used for testing in comparison to an existing method.

Results: Recommended ICA components from our algorithm agreed better with tb-fMRI language localizations, demonstrating significantly higher Dice coefficients and Pearson correlation scores in left hemisphere primary language areas.

Impact: The proposed method can improve the accuracy of automated detection of rs-fMRI language network. This may benefit presurgical evaluation for patients whose tumors are adjacent to language areas but have limited tb-fMRI.

Keywords: Task/Intervention Based fMRI, fMRI (resting state), Obsessive compulsive disorder, Focused Ultrasound, Surgery

Motivation: MR-guided focused ultrasound capsulotomy is a surgical procedure to treat intractable OCD.  While demonstrating clinical success, the mechanisms of symptom decrease are poorly understood.

Goal(s): This study sought to explore how intrinsic brain networks change following surgery.

Approach: Seed-based resting state fMRI was used to analyze intrinsic networks in a group of 6 OCD patients prior to and following surgery, along with a matched control group.

Results: Prior to surgery, OCD patients showed greater connectivity within internally-focused networks, and less connectivity in those involved in external cognition.  One year following surgery, these connectivity differences were substantially reduced relative to controls.

Impact: This study will appeal to neuroscientists interested in resting-state networks involved with OCD, as well as in how these networks change following a MRgFUS surgical procedure which produced substantial clinical improvement.

Keywords: Functional Connectivity, Brain Connectivity

Motivation: The mechanistic understanding of dysfunctional neural circuits involved in obsessive-compulsive disorder (OCD) is incomplete.

Goal(s): To replicate previous findings in an independent data set and replenish mechanisms of changes in functional connectivity architectures within neurocircuitry of OCD using resting-state fMRI.

Approach: Network-based statistical analysis on a brain network incorporating functionally parcellated regions of interest defined by clustering technique was used.

Results: Hyperconnectivity were detected in the fronto-pallidal, fronto-thalamic, basal ganglia-thalamic, intra-thalamic, and thalamo-amygdala connections in OCD patients compared with healthy controls.

Impact: We depict the neurocircuitry model of OCD pathophysiology through the functional network connectivity perspective and extend it by providing the importance of intra-thalamic and thalamo-amygdala connections in OCD. These findings add mechanistic insights to the dysfunctional neural circuits in OCD.

Keywords: Functional Connectivity, fMRI (resting state), Epilepsy, Functional Connectivity

Motivation: Epileptic tissue generates interictal spikes between seizures, which are used to localize the epileptic focus clinically.

Goal(s): We aimed to detect dynamic functional connectivity (FC) patterns in resting-state fMRI data that may be related to interictal spikes.

Approach: We detected whole-brain dynamic FC patterns at timepoints that had FC characteristics similar to epileptic spikes in both healthy controls and patients with temporal lobe epilepsy (TLE).

Results: We found three dynamic FC patterns, one of which occurred more in TLE than in controls and the occurrence of which was related to clinical measures of epilepsy severity.

Impact: These results suggest the potential clinical utility of fMRI-based dynamic FC to detect interictal spikes. Future studies can evaluate the correspondence of these dynamic FC patterns to interictal spikes using simultaneous electrophysiology and fMRI.

Keywords: Functional Connectivity, Alzheimer's Disease, intermittent theta-burst stimulation (iTBS)

Motivation: In Alzheimer's disease (AD), the neuropsychiatric inventory is strongly affected by sleep disorders, and vice versa. Central thalamic deep brain stimulation (CT-iTBS) has improved memory and altered the hypothalamic function which may impact the orexinergic system and sleep.

Goal(s): Investigating the therapeutic effect of CT-iTBS on sleep, orexinergic system, and cognitive function in AD.

Approach: Applied functional magnetic resonance imaging, electrocorticogram-electromyogram, behavioral tasks, and ELISA to assess the impact of CT-iTBS in 3xTgAD mouse model.

Results: CT-iTBS significantly improved sleep fragmentation, functional connectivity, cognitive function, and orexin receptors and concentrations in 3xTgAD mice.

Impact: We discovered that CT-iTBS may play an important role in modulating sleep, the orexinergic system, and cognitive function in AD. Improved outcomes pave the future direction of treating sleep disorders in Alzheimer's disease.

Keywords: Functional Connectivity, Alzheimer's Disease

Motivation: Cerebellar involvement in Alzheimer’s disease (AD) has not been studied to the extent that cortical neuropathological changes have been. Historical and recent histopathological literature demonstrates cerebellar AD pathology while functional investigations have demonstrated disrupted intrinsic cortical – cerebellar connectivity in AD.

Goal(s): Investigate metabolic activity and functional connectivity of the cerebellum with the default mode network, dorsal attention network, and primary olfactory cortex.

Approach: Characterizing the cerebellum’s metabolic activity using 18F-fluorodeoxyglucose positron data from the Alzheimer’s Disease Neuroimaging Initiative.

Results: In contrast to known parietal and temporal lobe FDG hypo-metabolism in AD, significant FDG hyper-metabolism was found in the cerebellum.

Impact: Results show that resting state functional connectivity of cerebellar regions (that show hyper FDG metabolic activity) is impaired across brain-wide networks. Future work focusing on inhibitory control of the cerebellum as a potential pathway of AD pathogenesis is warranted.

Keywords: fMRI Analysis, Alzheimer's Disease, Resting state fMRI, Empirical Mode Decomposition, Time frequency analysis

Motivation: The time frequency analysis of brain networks in resting state fMRI has largely been based on linear decompositions.

Goal(s): The primary goal of this study is to analyze the temporal dynamics of these networks using an adaptive nonlinear approach devoid of any apriori assumptions or basis functions.

Approach: Empirical Mode Decomposition (EMD), a data driven technique is utilized to investigate the energy period relationship differences in brain networks across cognitively normal (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD).

Results: The AD group operates at a higher frequency with reduced energy in typical resting state networks compared to both CN and MCI. 

Impact: The time varying energy and period profiles obtained from EMD could serve as a potential neuromarker for disease progression from MCI to AD, resulting in timely and early clinical intervention. 

Keywords: Task/Intervention Based fMRI, Alzheimer's Disease, pupil dynamics

Motivation: The pupil-fMRI correlation analysis reveals that erroneous pupillary light responses in AD mice are highly correlated to specific neuromodulatory systems. 

Goal(s): This study applied an explainable AI method with a pre-trained deep convolutional neural network to process pupil-fMRI interactive measurements of awake mice to verify AD biomarkers. 

Approach: Using the GradCAM method, we produced the saliency heatmap, which can be used to verify the underlying responsible functional nuclei for classification that could be impaired due to AD degeneration.

Results: This study applied a novel GradCAM-based machine learning scheme to elucidate AD-specific pupillary responses based on impaired neuromodulatory dysfunction as a non-invasive AD biomarker.

Impact: The GradCAM-based saliency map obtained with an XAI method could be used to verify the statistical differential maps of PLR-based fMRI correlation between AD and WT mice, providing a novel non-invasive AD bioimaging marker.