ECT-induced cognitive side effects are associated with hippocampal enlargement

Electroconvulsive therapy (ECT) is of the most effective treatments available for treatment-resistant depression, yet it is underutilized in part due to its reputation of causing cognitive side effects in a significant number of patients. Despite intensive neuroimaging research on ECT in the past two decades, the underlying neurobiological correlates of cognitive side effects remain elusive. Because the primary ECT-related cognitive deficit is memory impairment, it has been suggested that the hippocampus may play a crucial role. In the current study, we investigated 29 subjects with longitudinal MRI and detailed neuropsychological testing in two independent cohorts (N = 15/14) to test if volume changes were associated with cognitive side effects. The two cohorts underwent somewhat different ECT study protocols reflected in electrode placements and the number of treatments. We used longitudinal freesurfer algorithms (6.0) to obtain a bias-free estimate of volume changes in the hippocampus and tested its relationship with neurocognitive score changes. As an exploratory analysis and to evaluate how specific the effects were to the hippocampus, we also calculated this relationship in 41 other areas. In addition, we also analyzed cognitive data from a group of healthy volunteers (N = 29) to assess practice effects. Our results supported the hypothesis that hippocampus enlargement was associated with worse cognitive outcomes, and this result was generalizable across two independent cohorts with different diagnoses, different electrode placements, and a different number of ECT sessions. We found, in both cohorts, that treatment robustly increased the volume size of the hippocampus (Cohort 1: t = 5.07, Cohort 2: t = 4.82; p < 0.001), and the volume increase correlated with the neurocognitive T-score change. (Cohort 1: r = −0.68, p = 0.005; Cohort 2: r = −0.58; p = 0.04). Overall, our research indicates that novel treatment methods serving to avoid hippocampal volume increase may result in a better side effect profile.

CSF neurofilament light chain predicts 10-year clinical and radiologic worsening in multiple sclerosis

Background Neurofilament light chain (NfL) is an attractive biomarker of disease activity and progression in MS, but there is a lack in long-term prognostic data. Objective To test the long-term clinical and radiological prognostic value of cerebrospinal fluid (CSF)-NfL among newly diagnosed patients with MS. Methods Newly diagnosed MS patients where followed prospectively with baseline CSF-NfL and repeated MRI and clinical assessments for up to 10 years. Associations between baseline CSF-NfL and longitudinal MRI and clinical assessments were found by Generalized Estimating Equations analysis. Results Forty-two participants were included. CSF-NfL at baseline was significantly associated with the rate of atrophy in globus pallidus ( p = 0.009) and hippocampus ( p = 0.001) as evaluated by MRI. Baseline volumes of thalamus (β −0.33; 95% CI −0.57 to −0.10, p = 0.006), T1 (β 0.28; 95% CI 0.11 to 0.44, p = 0.001) and T2 (β 0.16; 95% CI 0.04 to 0.27, p = 0.008) lesions and baseline levels of CSF-NfL (β 0.9; 95% CI 0.3 to 1.5, p = 0.002) significantly predicted EDSS worsening over 10 years. Baseline CSF-NfL gave a comparable prediction to the best MRI volumetric predictors. Conclusion CSF-NfL predicted the clinical and radiological course of newly diagnosed patients with MS over a 10-year period, underlining its prognostic role.

Topological relationships between perivascular spaces and progression of white matter hyperintensities: a pilot study in a sample of the Lothian Birth Cohort 1936

Enlarged perivascular spaces (PVS) and white matter hyperintensities (WMH) are features of cerebral small vessel disease which can be seen in brain magnetic resonance imaging (MRI). Given the associations and proposed mechanistic link between PVS and WMH, they are hypothesised to also have topological proximity. However, this, and the influence of their spatial proximity on WMH progression are unknown. We analysed longitudinal MRI data from 29/32 participants (mean age at baseline = 71.9 years) in a longitudinal study of cognitive ageing, from three waves of data collection at 3-year intervals, alongside semi-automatic segmentation masks for PVS and WMH, to assess relationships. The majority of deep WMH clusters were found adjacent to or enclosing PVS (Wave 1: 77%; 2: 76%; 3: 69%), especially in frontal, parietal and temporal regions. Of the WMH clusters in the deep white matter that increased between waves, most increased around PVS (Waves 1-2: 73%; 2-3: 72%). Formal statistical comparisons of severity of each if these two SVD markers yielded no associations between deep WMH progression and PVS proximity. These findings may suggest deep WMH clusters preferentially form and grow around PVS, possibly reflecting the consequences of impaired interstitial fluid drainage via PVS. The utility of these relationships as predictors of WMH progression remains unclear.

Common and unique structural plasticity after left and right hemisphere stroke

Strokes to the left and right hemisphere lead to distinctive behavioral profiles. Are left and right hemisphere strokes (LHS and RHS) associated with distinct or common poststroke neuroplasticity patterns? Understanding this issue would reveal hemispheric neuroplasticity mechanisms in response to brain damage. To this end, we investigated poststroke structural changes (2 weeks to 3 months post-onset) using longitudinal MRI data from 69 LHS and 55 RHS patients and 31 demographic-matched healthy control participants. Both LHS and RHS groups showed statistically common plasticity independent of the lesioned hemisphere, including 1) gray matter (GM) expansion in the ipsilesional and contralesional precuneus, and contralesional superior frontal gyrus; 2) GM shrinkage in the ipsilesional medial orbital frontal gyrus and middle cingulate cortex. On the other hand, only RHS patients had significant GM expansion in the ipsilesional medial superior and orbital frontal cortex. Importantly, these common and unique GM changes post-stroke largely overlapped with highly-connected cortical hub regions in healthy individuals. Moreover, they correlated with behavioral recovery, indicating that post-stroke GM volumetric changes in cortical hubs reflect compensatory rather than maladaptive mechanisms. These results highlight the importance of structural neuroplasticity in hub regions of the cortex, along with the hemispheric specificity, for stroke recovery.

How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases

BackgroundLongitudinal brain MRI monitoring in neurodegeneration potentially provides substantial insights into the temporal dynamics of the underlying biological process, but is time- and cost-intensive and may be a burden to patients with disabling neurological diseases. Thus, the conceptualization of follow-up time-intervals in longitudinal MRI studies is an essential challenge and substantial for the results. The objective of this work is to discuss the association of time-intervals and the results of longitudinal trends in the frequently used design of one baseline and two follow-up scans.MethodsDifferent analytical approaches for calculating the linear trend of longitudinal parameters were studied in simulations including their performance of dealing with outliers; these simulations were based on the longitudinal striatum atrophy in MRI data of Huntington’s disease patients, detected by atlas-based volumetry (ABV).ResultsFor the design of one baseline and two follow-up visits, the simulations with outliers revealed optimum results for identical time-intervals between baseline and follow-up scans. However, identical time-intervals between the three acquisitions lead to the paradox that, depending on the fit method, the first follow-up scan results do not influence the final results of a linear trend analysis.ConclusionsThis theoretical study analyses how the design of longitudinal imaging studies with one baseline and two follow-up visits influences the results. Suggestions for the analysis of longitudinal trends are provided.