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The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing. Areas of interest include medical and biological imaging, digital pathology, molecular imaging, microscopy, and associated computational imaging, image analysis, and image-guided treatment, alongside physiological signal processing, computational biology, and bioinformatics.

Epidural electrical stimulation (EES) targeting the dorsal roots of lumbosacral segments restores walking in people with spinal cord injury (SCI). However, EES is delivered with multielectrode paddle leads that were originally designed to target the dorsal column of the spinal cord. Here, we hypothesized that an arrangement of electrodes targeting the ensemble of dorsal roots involved in leg and trunk movements would result in superior efficacy, restoring more diverse motor activities after the most severe SCI. To test this hypothesis, we established a computational framework that informed the optimal arrangement of electrodes on a new paddle lead and guided its neurosurgical positioning. We also developed software supporting the rapid configuration of activity-specific stimulation programs that reproduced the natural activation of motor neurons underlying each activity. We tested these neurotechnologies in three individuals with complete sensorimotor paralysis as part of an ongoing clinical trial (www.clinicaltrials.govidentifier NCT02936453). Within a single day, activity-specific stimulation programs enabled these three individuals to stand, walk, cycle, swim and control trunk movements. Neurorehabilitation mediated sufficient improvement to restore these activities in community settings, opening a realistic path to support everyday mobility with EES in people with SCI.

Quantitative spinal cord (SC) magnetic resonance imaging (MRI) presents many challenges, including a lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine genericprotocol, for users of 3T MRI systems from the three main manufacturers: GE, Philips and Siemens. The protocol provides guidance for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area computation, multi-echo gradient echo for gray matter cross-sectional area, and magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. In a companion paper from the same authors, the spine genericprotocol was used to acquire data across 42 centers in 260 healthy subjects. The key details of the spine genericprotocol are also available in an open-access document that can be found at https://github.com/spine-generic/protocols. The protocol will serve as a starting point for researchers and clinicians implementing new SC imaging initiatives so that, in the future, inclusion of the SC in neuroimaging protocols will be more common. The protocol could be implemented by any trained MR technician or by a researcher/clinician familiar with MRI acquisition.

Alterations in activity and connectivity of brain circuits implicated in emotion processing and emotion regulation have been observed during resting-state for different clinical phases of bipolar disorders (BD), but longitudinal investigations across different mood states in the same patients are still rare. Furthermore, measuring dynamics of functional connectivity patterns offers a powerful method to explore changes in the brain’s intrinsic functional organization across mood states. We used a novel co-activation pattern (CAP) analysis to explore the dynamics of amygdala connectivity at rest in a cohort of 20 BD patients prospectively followed-up and scanned across distinct mood states: euthymia (20 patients; 39 sessions), depression (12 patients; 18 sessions), or mania/hypomania (14 patients; 18 sessions). We compared them to 41 healthy controls scanned once or twice (55 sessions). We characterized temporal aspects of dynamic fluctuations in amygdala connectivity over the whole brain as a function of current mood. We identified six distinct networks describing amygdala connectivity, among which an interoceptive-sensorimotor CAP exhibited more frequent occurrences during hypomania compared to other mood states, and predicted more severe symptoms of irritability and motor agitation. In contrast, a default-mode CAP exhibited more frequent occurrences during depression compared to other mood states and compared to controls, with a positive association with depression severity. Our results reveal distinctive interactions between amygdala and distributed brain networks in different mood states, and foster research on interoception and default-mode systems especially during the manic and depressive phase, respectively. Our study also demonstrates the benefits of assessing brain dynamics in BD.

A growing body of research in the past decade has revealed that functional interaction between brain regions entail subject-specific idiosyncrasies that are highly replicable. As such, functional connectivity patterns can be seen as an individual's brain fingerprint, enabling their identification within a population, in health and disease. The conventional method involves constructing the functional connectome by treating brain regions as vertices and utilizing pairwise measures of statistical dependence, such as Pearson's correlation coefficient, between the regional time-courses as edge weights. However, by focusing on EEG data in our study, we propose an alternative approach to learn a sparse graph structure from an individual's EEG data using principles from graph signal processing. The inferred subject-specific graphs encode subtle instantaneous spatial relations between the ensemble set of EEG electrodes in such way that EEG maps are seen as smooth functions residing on the graph. We validated the inferred graphs on two publicly available EEG datasets, demonstrating that the learned graphs outperform correlation-based functional connectomes in fingerprinting performance. This talk provides an overview of our proposed method and related results, which was presented at the 2023 European Signal Processing Conference in Helsinki, Finland. The work was selected as the second-best student paper; aside from the talk, a poster was presented as part of the contest, segments of which can be found as figures in the present article.

Compared with conventional unimodal analysis, understanding how brain function and structure relate to one another opens a new biologically relevant assessment of neural mechanisms. However, how function-structure dependencies (FSDs) evolve throughout typical and abnormal neurodevelopment remains elusive. The 22q11.2 deletion syndrome (22q11.2DS) offers an important opportunity to study the development of FSDs and their specific association with the pathophysiology of psychosis.

22q11.2 deletion syndrome (22q11DS) is the third-largest known genetic risk factor for the development of psychosis. Dysconnectivity has consistently been implicated in the physiopathology of psychosis. Structural covariance of cortical morphology is a method of exploring connectivity among brain regions that to date has not been employed in 22q11DS.

Prodromal positive psychotic symptoms and anxiety are two strong risk factors for schizophrenia in 22q11.2 deletion syndrome (22q11DS). The analysis of large-scale brain network dynamics during rest is promising to investigate aberrant brain function and identify potentially more reliable biomarkers.

Alzheimer’s disease (AD) is characterized by amyloidosis of brain tissues. This phenomenon is studied with genetically-modified mouse models. We propose a method to quantify amyloidosis in whole 5xFAD mouse brains, a model of AD. We use optical projection tomography (OPT) and a random forest voxel classifier to segment and measure amyloid plaques. We validate our method in a preliminary cross-sectional study, where we measure 6136 ± 1637, 8477 ± 3438, and 17267 ± 4241 plaques (AVG ± SD) at 11, 17, and 31 weeks. Overall, this method can be used in the evaluation of new treatments against AD.

AbstractUsing electroencephalography (EEG) to elucidate the spontaneous activation of brain resting-state networks (RSNs) is nontrivial as the signal of interest is of low amplitude and it is difficult to distinguish the underlying neural sources. Using the principles of electric field topographical analysis, it is possible to estimate the meta-stable states of the brain (i.e., the resting-state topographies, so-called microstates). We estimated seven resting-state topographies explaining the EEG data set with k-means clustering (N= 164, 256 electrodes). Using a method specifically designed to localize the sources of broadband EEG scalp topographies by matching sensor and source space temporal patterns, we demonstrated that we can estimate the EEG RSNs reliably by measuring the reproducibility of our findings. After subtracting their mean from the seven EEG RSNs, we identified seven state-specific networks. The mean map includes regions known to be densely anatomically and functionally connected (superior frontal, superior parietal, insula, and anterior cingulate cortices). While the mean map can be interpreted as a “router,” crosslinking multiple functional networks, the seven state-specific RSNs partly resemble and extend previous functional magnetic resonance imaging-based networks estimated as the hemodynamic correlates of four canonical EEG microstates.

In recent years, three-dimensional mesoscopic imaging has gained significant importance in life sciences for fundamental studies at the whole-organ level. In this manuscript, we present an optical projection tomography (OPT) method designed for imaging of the intact mouse brain. The system features an isotropic resolution of ~50 µm and an acquisition time of four to eight minutes, using a 3-day optimized clearing protocol. Imaging of the brain autofluorescence in 3D reveals details of the neuroanatomy, while the use of fluorescent labels displays the vascular network and amyloid deposition in 5xFAD mice, an important model of Alzheimer’s disease (AD). Finally, the OPT images are compared with histological slices.

Normal aging is associated with brain atrophy and cognitive decline. Working memory, involved in cognitive functioning and daily living, is particularly affected. Music training gained momentum in research on brain plasticity and possible transfer effects of interventions on working memory, especially in the context of healthy aging. This longitudinal voxel-based morphometry study evaluated effects of 6-month music interventions on grey matter volume plasticity and auditory working memory performance in 132 healthy older adults. This study is part of a randomized controlled trial comparing two interventions: piano practice (experimental group) and musical culture (musical listening awareness, active control). We report significant grey matter volume increase at whole-brain level in the caudate nucleus, Rolandic operculum and inferior cerebellum when merging both groups, but no group differences. Cerebellar grey matter increase, training intensity metrics and sleep were positively associated with tonal working memory improvement. Digit Span Backward verbal working memory performance also increased. Using region of interest analyses, we showed a group difference in the right primary auditory cortex grey matter volume, decreasing in the musical group while staying stable in the piano group. In contrast, a significant 6-month whole-brain atrophy pattern consistent with longer-term investigations of the aging brain was revealed. We argue that education for seniors should become a major policy priority in the framework of healthy aging, to promote brain plasticity and cognitive reserve, through stimulating group interventions such as music-making and active listening.