Subject motion can cause artifacts in clinical MRI, frequently necessitating repeat scans. We propose to alleviate this inefficiency by predicting artifact scores from partial multi-shot multi-slice acquisitions, which may guide the operator in aborting corrupted scans early.

OBJECTIVE: To investigate how the presence/side of hippocampal sclerosis (HS) are related to the white matter structure of cingulum bundle (CB), arcuate fasciculus (AF), and inferior longitudinal fasciculus (ILF) in mesial temporal lobe epilepsy (MTLE).

METHODS: We acquired diffusion-weighted magnetic resonance imaging (MRI) from 86 healthy and 71 individuals with MTLE (22 righ-HS; right-HS, 34 left-HS; left-HS, and 15 nonlesional MTLE). We utilized two-tensor tractography and fiber clustering to compare fractional anisotropy (FA) of each side/tract between groups. Additionally, we examined the association between FA and nonverbal (WMS-R) and verbal (WMS-R, RAVLT codification) memory performance for MTLE individuals.

RESULTS: White matter abnormalities depended on the side and presence of HS. The left-HS demonstrated widespread abnormalities for all tracts, the right-HS showed lower FA for ipsilateral tracts and the nonlesional MTLE group did not differ from healthy individuals. Results indicate no differences in verbal/nonverbal memory performance between the groups, but trend-level associations between higher FA of visual memory and the left CB (r = 0.286, P = 0.018), verbal memory (RAVLT) and -left CB (r = 0.335, P = 0.005), -right CB (r = 0.286, P = 0.016), and -left AF (r = 0.287, P = 0.017).

SIGNIFICANCE: Our results highlight that the presence and side of HS are crucial to understand the pathophysiology of MTLE. Specifically, left-sided HS seems to be related to widespread bilateral white matter abnormalities. Future longitudinal studies should focus on developing diagnostic and treatment strategies dependent on HS's presence/side.

BACKGROUND: Depression in Parkinson's disease (PD) is common, disabling and responds poorly to standard antidepressant medication. Motivational symptoms of depression, such as apathy and anhedonia, are particularly prevalent in depression in PD and predict poor response to antidepressant treatment. Loss of dopaminergic innervation of the striatum is associated with emergence of motivational symptoms in PD, and mood fluctuations correlate with dopamine availability. Accordingly, optimising dopaminergic treatment for PD can improve depressive symptoms, and dopamine agonists have shown promising effects in improving apathy. However, the differential effect of antiparkinsonian medication on symptom dimensions of depression is not known.

AIMS: We hypothesised that there would be dissociable effects of dopaminergic medications on different depression symptom dimensions. We predicted that dopaminergic medication would specifically improve motivational symptoms, but not other symptoms, of depression. We also hypothesised that antidepressant effects of dopaminergic medications with mechanisms of action reliant on pre-synaptic dopamine neuron integrity would attenuate as pre-synaptic dopaminergic neurodegeneration progresses.

METHODS: We analysed data from a longitudinal study of 412 newly diagnosed PD patients followed over five years in the Parkinson's Progression Markers Initiative cohort. Medication state for individual classes of Parkinson's medications was recorded annually. Previously validated "motivation" and "depression" dimensions were derived from the 15-item geriatric depression scale. Dopaminergic neurodegeneration was measured using repeated striatal dopamine transporter (DAT) imaging.

RESULTS: Linear mixed-effects modelling was performed across all simultaneously acquired data points. Dopamine agonist use was associated with relatively fewer motivation symptoms as time progressed (interaction: β=-0.07, 95%CI [-0.13,-0.01], p=0.015) but had no effect on the depression symptom dimension (p=0.6). In contrast, monoamine oxidase-B (MAO-B) inhibitor use was associated with relatively fewer depression symptoms across all years (β=-0.41, 95%CI [-0.81,-0.01], p=0.047). No associations were observed between either depression or motivation symptoms and levodopa or amantadine use. There was a significant interaction between striatal DAT binding and MAO-B inhibitor use on motivation symptoms: MAO-B inhibitor use was associated with lower motivation symptoms in patients with higher striatal DAT binding (interaction: β=-0.24, 95%CI [-0.43,-0.05], p=0.012). No other medication effects were moderated by striatal DAT binding measures.

CONCLUSIONS: We identified dissociable associations between dopaminergic medications and different dimensions of depression in PD. Dopamine agonists may be effective for treatment of motivational symptoms of depression. In contrast, MAO-B inhibitors may improve both depressive and motivation symptoms, albeit the latter effect appears to be attenuated in patients with more severe striatal dopaminergic neurodegeneration, which may be a consequence of dependence on pre-synaptic dopaminergic neuron integrity.

Atlases are crucial to imaging statistics as they enable the standardization of inter-subject and inter-population analyses. While existing atlas estimation methods based on fluid/elastic/diffusion registration yield high-quality results for the human brain, these deformation models do not extend to a variety of other challenging areas of neuroscience such as the anatomy of C. elegans worms and fruit flies. To this end, this work presents a general probabilistic deep network-based framework for atlas estimation and registration which can flexibly incorporate various deformation models and levels of keypoint supervision that can be applied to a wide class of model organisms. Of particular relevance, it also develops a deformable piecewise rigid atlas model which is regularized to preserve inter-observation distances between neighbors. These modeling considerations are shown to improve atlas construction and key-point alignment across a diversity of datasets with small sample sizes including neuron positions in C. elegans hermaphrodites, fluorescence microscopy of male C. elegans, and images of fruit fly wings. Code is accessible at https://github.com/amin-nejat/Deformable-Atlas.

Atlases are crucial to imaging statistics as they enable the standardization of inter-subject and inter-population analyses. While existing atlas estimation methods based on fluid/elastic/diffusion registration yield high-quality results for the human brain, these deformation models do not extend to a variety of other challenging areas of neuroscience such as the anatomy of C. elegans worms and fruit flies. To this end, this work presents a general probabilistic deep network-based framework for atlas estimation and registration which can flexibly incorporate various deformation models and levels of keypoint supervision that can be applied to a wide class of model organisms. Of particular relevance, it also develops a deformable piecewise rigid atlas model which is regularized to preserve inter-observation distances between neighbors. These modeling considerations are shown to improve atlas construction and key-point alignment across a diversity of datasets with small sample sizes including neuron positions in C. elegans hermaphrodites, fluorescence microscopy of male C. elegans, and images of fruit fly wings. Code is accessible at https://github.com/amin-nejat/Deformable-Atlas.