Objective: To further evaluate the relationship between the clinical profiles and limbic and motor brain regions and their connecting pathways in psychogenic nonepileptic seizures (PNES). Neurite Orientation Dispersion and Density Indices (NODDI) multicompartment modeling was used to test the relationships between tissue alterations in patients with traumatic brain injury (TBI) and multiple psychiatric symptoms. Methods: The sample included participants with prior TBI (TBI; N = 37) but no PNES, and with TBI and PNES (TBI + PNES; N = 34). Participants completed 3T Siemens Prisma MRI high angular resolution imaging diffusion protocol. Statistical maps, including fractional anisotropy (FA), mean diffusivity (MD), neurite dispersion [orientation dispersion index (ODI)] and density [intracellular volume fraction (ICVF), and free water (i.e., isotropic) volume fraction (V-ISO)] signal intensity, were generated for each participant. Linear mixed-effects models identified clusters of between-group differences in indices of white matter changes. Pearson's r correlation tests assessed any relationship between signal intensity and psychiatric symptoms. Results: Compared to TBI, TBI + PNES revealed decreases in FA, ICVF, and V-ISO and increases in MD for clusters within cingulum bundle, uncinate fasciculus, fornix/stria terminalis, and corticospinal tract pathways (cluster threshold a = 0.05). Indices of white matter changes for these clusters correlated with depressive, anxiety, PTSD, psychoticism, and somatization symptom severity (FDR threshold a = 0.05). A follow-up within-group analysis revealed that these correlations failed to reach the criteria for significance in the TBI + PNES group alone. Interpretation: The results expand support for the hypothesis that alterations in pathways comprising the specific PNES network correspond to patient profiles. These findings implicate myelin-specific changes as possible contributors to PNES, thus introducing novel potential treatment targets.

Diffusion tensor imaging (DTI) studies show widespread white matter abnormalities in schizophrenia, but it is difficult to directly relate these parameters to biological processes. Neurite orientation dispersion and density imaging (NODDI) is geared toward biophysical characterization of white matter microstructure, but only few studies have leveraged this technique to study white matter alterations. We recruited 42 schizophrenia patients (30 antipsychotic-naïve and 12 currently untreated) and 42 matched controls in this prospective study. We assessed the orientation dispersion index (ODI) and extracellular free water (FW) using singleshell DTI data before and after a 6-week trial of risperidone. Longitudinal data were available for 27 patients. Voxelwise analyses showed significantly increased ODI in the posterior limb of the internal capsule in unmedicated patients (242 voxels; x = −24; y = 6; z = 6; p < 0.01; α < 0.04), but no alterations in FW. Whole brain measures did not reveal alterations in ODI but a 6.3% trend-level increase in FW in unmedicated SZ (t = −1.873; p = 0.07). Baseline ODI was negatively correlated with subsequent response to antipsychotic treatment (r = −0.38; p = 0.049). Here, we demonstrated altered fiber complexity in medication-naïve and unmedicated patients with a schizophrenia spectrum illness. Lesser whole brain fiber uniformity was predictive of poor response to treatment, suggesting this measure may be a clinically relevant biomarker. Interestingly, we found no significant changes in NODDI indices after short-term treatment with risperidone. Our data show that biophysical diffusion models have promise for the in vivo evaluation of brain microstructure in this devastating neuropsychiatric syndrome.

No abstract

Identification of individuals at high risk for rapid progression of motor and cognitive signs in Parkinson disease (PD) is clinically significant. Postural instability and gait dysfunction (PIGD) are associated with greater motor and cognitive deterioration. We examined the relationship between baseline clinical factors and the development of postural instability using 5-year longitudinal de-novo idiopathic data (n = 301) from the Parkinson’s Progressive Markers Initiative (PPMI). Logistic regression analysis revealed baseline features associated with future postural instability, and we designated this cohort the emerging postural instability (ePI) phenotype. We evaluated the resulting ePI phenotype rating scale validity in two held-out populations which showed a significantly higher risk of postural instability. Emerging PI phenotype was identified before onset of postural instability in 289 of 301 paired comparisons, with a median progression time of 972 days. Baseline cognitive performance was similar but declined more rapidly in ePI phenotype. We provide an ePI phenotype rating scale (ePIRS) for evaluation of individual risk at baseline for progression to postural instability.

No abstract