sGraSP: A graph-based method for the derivation of subject-specific functional parcellations of the brain

Functional parcellation of the hippocampus by semi-supervised clustering of resting state fMRI data

Many unsupervised methods are widely used for parcellating the brain. However, unsupervised methods aren’t able to integrate prior information, obtained from such as exiting functional neuroanatomy studies, to parcellate the brain, whereas the prior information guided semi-supervised method can generate more reliable brain parcellation. In this study, we propose a novel semi-supervised clustering method for parcellating the brain into spatially and functionally consistent parcels based on resting state functional magnetic resonance imaging (fMRI) data. Particularly, the prior supervised and spatial information is integrated into spectral clustering to achieve reliable brain parcellation. The proposed method has been validated in the hippocampus parcellation based on resting state fMRI data of 20 healthy adult subjects. The experimental results have demonstrated that the proposed method could successfully parcellate the hippocampus into head, body and tail parcels. The distinctive functional connectivity patterns of these parcels have further demonstrated the validity of the parcellation results. The effects of aging on the three hippocampus parcels’ functional connectivity were also explored across the healthy adult subjects. Compared with state-of-the-art methods, the proposed method had better performance on functional homogeneity. Furthermore, the proposed method had good test–retest reproducibility validated by parcellating the hippocampus based on three repeated resting state fMRI scans from 24 healthy adult subjects.

Personalizing neuromodulation

In the era of "big data", we are gaining rich person-specific information about neuroanatomy, neural function, and cognitive functions. However, the optimal ways to create precise approaches to optimize individuals' mental functions in health and disease are unclear. Multimodal analysis and modeling approaches can guide neuromodulation by combining anatomical networks, functional signal analysis, and cognitive neuroscience paradigms in single subjects. Our progress could be improved by progressing from statistical fits to mechanistic models. Using transcranial magnetic stimulation as an example, we discuss how integrating methods with a focus on mechanisms could improve our predictions TMS effects within individuals, refine our models of health and disease, and improve our treatments.

Harnessing networks and machine learning in neuropsychiatric care

The development of next-generation therapies for neuropsychiatric illness will likely rely on a precise and accurate understanding of human brain dynamics. Toward this end, researchers have focused on collecting large quantities of neuroimaging data. For simplicity, we will refer to large cross-sectional neuroimaging studies as broad studies and to intensive longitudinal studies as deep studies. Recent progress in identifying illness subtypes and predicting treatment response in neuropsychiatry has been supported by these study designs, along with methods bridging machine learning and network science. Such methods combine analytic power, interpretability, and direct connection to underlying theory in cognitive neuroscience. Ultimately, we propose a general framework for the treatment of neuropsychiatric illness relying on the findings from broad and deep studies combined with basic cognitive and physiologic measurements.

A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging

Analysis of functional magnetic resonance imaging (fMRI) plays a pivotal role in uncovering an understanding of the brain. fMRI data contain both spatial volume and temporal signal information, which provide a depiction of brain activity. The analysis pipeline, however, is hampered by numerous uncertainties in many of the steps; often seen as one of the last hurdles for the domain. In this review, we categorise fMRI research into three pipeline phases: (i) image acquisition and processing; (ii) image analysis; and (iii) visualisation and human interpretation, to explore the uncertainties that arise in each phase, including the compound effects due to the inter-dependence of steps. Attempts at mitigating uncertainties rely on providing interactive visual analytics that aid users in understanding the effects of the uncertainties and adjusting their analyses. This impetus for visual analytics comes in light of considerable research investigating uncertainty throughout the pipeline. However, to the best of our knowledge, there is yet to be a comprehensive review on the importance and utility of uncertainty visual analytics (UVA) in addressing fMRI concerns, which we term fMRI-UVA. Such techniques have been broadly implemented in related biomedical fields, and its potential for fMRI has recently been explored; however, these attempts are limited in their scope and utility, primarily focussing on addressing small parts of single pipeline phases. Our comprehensive review of the fMRI uncertainties from the perspective of visual analytics addresses the three identified phases in the pipeline. We also discuss the two interrelated approaches for future research opportunities for fMRI-UVA.

Subject-Specific Structural Parcellations Based on Randomized AB-divergences

Brain parcellation provides a means to approach the brain in smaller regions. It also affords an appropriate dimensionality reduction in the creation of connectomes. Most approaches to creating connectomes start with registering individual scans to a template, which is then parcellated. Data processing usually ends with the projection of individual scans onto the parcellation for extracting individual biomarkers, such as connectivity signatures. During this process, registration errors can significantly alter the quality of biomarkers. In this paper, we propose to mitigate this issue with a hybrid approach for brain parcellation. We use diffusion MRI (dMRI) based structural connectivity measures to drive the refinement of an anatomical prior parcellation. Our method generates highly coherent structural parcels in native subject space while maintaining interpretability and correspondences across the population. This goal is achieved by registering a population-wide anatomical prior to individual dMRI scan and generating connectivity signatures for each voxel. The anatomical prior is then deformed by re-parcellating the brain according to the similarity between voxel connectivity signatures while constraining the number of parcels. We investigate a broad family of signature similarities known as AB-divergences and explain how a divergence adapted to our segmentation task can be selected. This divergence is used for parcellating a high-resolution dataset using two graph-based methods. The promising results obtained suggest that our approach produces coherent parcels and stronger connectomes than the original anatomical priors.