The success of deep brain stimulation (DBS) relies on applying carefully titrated therapeutic stimulation at specific targets. Once implanted, the electrical stimulation parameters at each electrode contact can be modified. Iteratively adjusting the stimulation parameters enables testing for the optimal stimulation settings. Due to the large parameter space, the currently employed empirical testing of individual parameters based on acute clinical response is not sustainable. Within the constraints of short clinical visits, optimization is particularly challenging when clinical features lack immediate feedback, as seen in DBS for dystonia and depression and with the cognitive and axial side effects of DBS for Parkinson’s disease. A personalized approach to stimulation parameter selection is desirable as the increasing complexity of modern DBS devices also expands the number of available parameters. This review describes three emerging imaging and electrophysiological methods of personalizing DBS programming. Normative connectome-base stimulation utilizes large datasets of normal or disease-matched connectivity imaging. The stimulation location for an individual patient can then be varied to engage regions associated with optimal connectivity. Electrophysiology-guided open- and closed-loop stimulation capitalizes on the electrophysiological recording capabilities of modern implanted devices to individualize stimulation parameters based on biomarkers of success or symptom onset. Finally, individual functional MRI (fMRI)-based approaches use fMRI during active stimulation to identify parameters resulting in characteristic patterns of functional engagement associated with long-term treatment response. Each method provides different but complementary information, and maximizing treatment efficacy likely requires a combined approach.

Despite depression being a leading cause of global disability, neuroimaging studies have struggled to identify replicable neural correlates of depression or explain limited variance. This challenge may, in part, stem from the intertwined state (current symptoms; variable) and trait (general propensity; stable) experiences of depression.

Here, we sought to disentangle state from trait experiences of depression by leveraging a longitudinal cohort and stratifying individuals into four groups: those in remission (‘trait depression group’), those with large longitudinal severity changes in depression symptomatology (‘state depression group’), and their respective matched control groups (total analytic n = 1030). We hypothesized that spatial network organization would be linked to trait depression due to its temporal stability, whereas functional connectivity between networks would be more sensitive to state-dependent depression symptoms due to its capacity to fluctuate.

We identified 15 large-scale probabilistic functional networks from resting-state fMRI data and performed group comparisons on the amplitude, connectivity, and spatial overlap between these networks, using matched control participants as reference. Our findings revealed higher amplitude in visual networks for the trait depression group at the time of remission, in contrast to controls. This observation may suggest altered visual processing in individuals predisposed to developing depression over time. No significant group differences were observed in any other network measures for the trait-control comparison, nor in any measures for the state-control comparison. These results underscore the overlooked contribution of visual networks to the psychopathology of depression and provide evidence for distinct neural correlates between state and trait experiences of depression.

Portable MRI for neuroimaging research in remote field settings can reach populations previously excluded from research, including communities underrepresented in current brain neuroscience databases and marginalized in health care. However, research conducted far from a medical institution and potentially in populations facing barriers to health care access raises the question of how to manage incidental findings (IFs) that may warrant clinical workup. Researchers should not withhold information about IFs from historically excluded and underserved population when members consent to receive it, and instead should facilitate access to information and a pathway to clinical care.

Portable MRI (pMRI) technology, which promises to transform brain imaging research by facilitating scanning in new geographic areas and the participation of new, diverse populations, raises many ethical, legal, and societal issues (ELSI). To understand this emerging pMRI ELSI landscape, we surveyed expert stakeholder views on ELSI challenges and solutions associated with pMRI research.

The paucity of existing baseline data for understanding neurologic health and the effects of injury on people from Indigenous populations is causally related to the limited representation of communities in neuroimaging research to date. In this paper, we explore ways to change this trend in the context of portable MRI, where portability has opened up imaging to communities that have been neglected or inaccessible in the past. We discuss pathways to engage local leadership, foster the participation of communities for this unprecedented opportunity, and empower field-based researchers to bring the holistic worldview embraced by Indigenous communities to neuroimaging research.

Highly portable and accessible MRI technology will allow researchers to conduct field-based MRI research in community settings. Previous guidance for researchers working with fixed MRI does not address the novel ethical, legal, and societal issues (ELSI) of portable MRI (pMRI). Our interdisciplinary Working Group (WG) previously identified 15 core ELSI challenges associated with pMRI research and recommended solutions. In this article, we distill those detailed recommendations into a Portable MRI Research ELSI Checklist that offers practical operational guidance for researchers contemplating using this technology.

Summary: The aging of the population poses significant challenges in healthcare, necessitating innovative approaches. Advancements in brain imaging and artificial intelligence now allow for characterizing an individual’s state through their brain age,’’ derived from observable brain features. Exploring an individual’s biological age’’ rather than chronological age is becoming crucial to identify relevant clinical indicators and refine risk models for age-related diseases. However, traditional brain age measurement has limitations, focusing solely on brain structure assessment while neglecting functional efficiency.

Our study focuses on developing neurocognitive ages’’ specific to cognitive systems to enhance the precision of decline estimation. Leveraging international (NKI2, ADNI) and Canadian (CIMA- Q, COMPASS-ND) databases with neuroimaging and neuropsychological data from older adults [control subjects with no cognitive impairment (CON): n = 1811; people living with mild cognitive impairment (MCI): n = 1341; with Alzheimer’s disease (AD): n= 513], we predicted individual brain ages within groups. These estimations were enriched with neuropsychological data to generate specific neurocognitive ages. We used longitudinal statistical models to map evolutionary trajectories. Comparing the accuracy of neurocognitive ages to traditional brain ages involved statistical learning techniques and precision measures.

The results demonstrated that neurocognitive age enhances the prediction of individual brain and cognition change trajectories related to aging and dementia. This promising approach could strengthen diagnostic reliability, facilitate early detection of at-risk profiles, and contribute to the emergence of precision gerontology/geriatrics.