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Motor neuron disease (MND) is a fatal progressive neurodegenerative disorder characterized by the breakdown of the motor system. The clinical spectrum of MND encompasses different phenotypes classified according to the relative involvement of the upper or lower motor neurons (LMN) and the presence of genetic or cognitive alterations, with clear prognostic implications. However, the pathophysiological differences of these phenotypes remain largely unknown. Recently, magnetic resonance imaging (MRI) has been recognized as a helpful in-vivo MND biomarker. An increasing number of studies is applying advanced neuroimaging techniques in order to elucidate the pathophysiological processes and to identify quantitative outcomes to be used in clinical trials. Diffusion tensor imaging (DTI) is a non-invasive method to detect white matter alterations involving the upper motor neuron and extra-motor white matter tracts. According to this background, the aim of this review is to highlight the key role of MRI and especially DTI, summarizing cross-sectional and longitudinal results of different approaches applied in MND. Current literature suggests that DTI is a promising tool in order to define anatomical "signatures" of the different phenotypes of MND and to track in vivo the progressive spread of pathological proteins aggregates.

We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan. Convolutional neural networks (CNNs) were applied on 3D T1-weighted images from ADNI and subjects recruited at our Institute (407 healthy controls [HC], 418 AD, 280 c-MCI, 533 stable MCI [s-MCI]). CNN performance was tested in distinguishing AD, c-MCI and s-MCI. High levels of accuracy were achieved in all the classifications, with the highest rates achieved in the AD vs HC classification tests using both the ADNI dataset only (99%) and the combined ADNI + non-ADNI dataset (98%). CNNs discriminated c-MCI from s-MCI patients with an accuracy up to 75% and no difference between ADNI and non-ADNI images. CNNs provide a powerful tool for the automatic individual patient diagnosis along the AD continuum. Our method performed well without any prior feature engineering and regardless the variability of imaging protocols and scanners, demonstrating that it is exploitable by not-trained operators and likely to be generalizable to unseen patient data. CNNs may accelerate the adoption of structural MRI in routine practice to help assessment and management of patients., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: To investigate differences in toe clearance between people with PD and age-matched healthy elderly (HE) during comfortable walking and to study the effects of dual-tasking and the use of an attentional strategy emphasizing heel strike on toe clearance., Design: Observational cross-sectional study., Setting: Camera-based 3D gait laboratory., Participants: Ten persons with PD (Hoehn and Yahr I to III) having mild gait disturbances and 10 HE., Interventions: Participants walked for two minutes under three conditions at comfortable pace: single-task walking, attending to heel strike during single-task walking, and dual-task walking., Main Outcome Measures: Minimal and maximal toe clearance; foot strike angle with the ground., Results: People with PD had less maximal toe clearance in the end of the swing phase and a smaller foot strike angle than HE during all three walking conditions. Impairments significantly diminished during heel strike focused walking improving performance to equal the HE. Heel strike focused walking resulted in an increased minimal toe clearance and a longer duration of end swing phase when compared to walking with and without a dual-task. The attentional strategy to focus on heel strike improved the stride length when compared to dual-task walking. Surprisingly, minimal toe clearance did not differ between PD and HE in any of the conditions and there were no dual-task effects on toe clearance., Conclusion: These findings provide evidence favoring the potential incorporation of an attentional strategy focusing on the heel strike in PD gait rehabilitation., (Copyright © 2017 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.)

Objective: To investigate functional brain network architecture in early-onset Alzheimer disease (EOAD) and behavioral variant frontotemporal dementia (bvFTD)., Methods: Thirty-eight patients with bvFTD, 37 patients with EOAD, and 32 age-matched healthy controls underwent 3D T1-weighted and resting-state fMRI. Graph analysis and connectomics assessed global and local functional topologic network properties, regional functional connectivity, and intrahemispheric and interhemispheric between-lobe connectivity., Results: Despite similarly extensive cognitive impairment relative to controls, patients with EOAD showed severe global functional network alterations (lower mean nodal strength, local efficiency, clustering coefficient, and longer path length), while patients with bvFTD showed relatively preserved global functional brain architecture. Patients with bvFTD demonstrated reduced nodal strength in the frontoinsular lobe and a relatively focal altered functional connectivity of frontoinsular and temporal regions. Functional connectivity breakdown in the posterior brain nodes, particularly in the parietal lobe, differentiated patients with EOAD from those with bvFTD. While EOAD was associated with widespread loss of both intrahemispheric and interhemispheric functional correlations, bvFTD showed a preferential disruption of the intrahemispheric connectivity., Conclusions: Disease-specific patterns of functional network topology and connectivity alterations were observed in patients with EOAD and bvFTD. Graph analysis and connectomics may aid clinical diagnosis and help elucidate pathophysiologic differences between neurodegenerative dementias., (Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Background: TANK-binding kinase 1 ( TBK1 ) gene has been recently identified as a causative gene of amyotrophic lateral sclerosis (ALS)., Methods: We sequenced the TBK1 gene in a cohort of 154 Italian patients with ALS with unclear genetic aetiology. We subsequently assessed the pathogenic potential of novel identified TBK1 variants using functional in vitro studies: expression, targeting and activity were evaluated in patient-derived fibroblasts and in cells transfected with mutated- TBK1 plasmids., Results: We identified novel genomic TBK1 variants including two loss-of-function (LoF) (p.Leu59Phefs*16 and c.358+5G>A), two missense (p.Asp118Asn and p.Ile397Thr) and one intronic variant (c.1644-5_1644-2delAATA), in addition to two previously reported pathogenetic missense variants (p.Lys291Glu and p.Arg357Gln). Functional studies in patient-derived fibroblasts revealed that the c.358+5G>A causes aberrant pre-mRNA processing leading TBK1 haploinsufficiency. Biochemical studies in cellular models showed that the truncating variant p.Leu59Phefs*16 abolishes TBK1 protein expression, whereas the p.Asp118Asn variant severely impairs TBK1 phosphorylation activity. Conversely, the p.Ile397Thr variant displayed enhanced phosphorylation activity, whose biological relevance is not clear., Conclusion: The observed frequency of TBK1 LoF variants was 1.3% (2/154), increasing up to 3.2% (5/154) by taking into account also the functional missense variants that we were able to classify as potentially pathogenic, supporting the relevance of TBK1 in the Italian population with ALS., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Purpose To investigate the structural brain connectome in patients with Parkinson disease (PD) and mild cognitive impairment (MCI) and in patients with PD without MCI. Materials and Methods This prospective study was approved by the local ethics committees, and written informed consent was obtained from all subjects prior to enrollment. The individual structural brain connectome of 170 patients with PD (54 with MCI, 116 without MCI) and 41 healthy control subjects was obtained by using deterministic diffusion-tensor tractography. A network-based statistic was used to assess structural connectivity differences among groups. Results Patients with PD and MCI had global network alterations when compared with both control subjects and patients with PD without MCI (range, P = .004 to P = .048). Relative to control subjects, patients with PD and MCI had a large basal ganglia and frontoparietal network with decreased fractional anisotropy (FA) in the right hemisphere and a subnetwork with increased mean diffusivity (MD) involving similar regions bilaterally (P < .01). When compared with patients with PD without MCI, those with PD and MCI had a network with decreased FA, including basal ganglia and frontotemporoparietal regions bilaterally (P < .05). Similar findings were obtained by adjusting for motor disability (P < .05, permutation-corrected P = .06). At P < .01, patients with PD and MCI did not show network alterations relative to patients with PD without MCI. Network FA and MD values were used to differentiate patients with PD and MCI from healthy control subjects and patients with PD without MCI with fair to good accuracy (cross-validated area under the receiver operating characteristic curve [principal + secondary connected components] range, 0.75-0.85). Conclusion A disruption of structural connections between brain areas forming a network contributes to determine an altered information integration and organization and thus cognitive deficits in patients with PD. These results provide novel information concerning the structural substrates of MCI in patients with PD and may offer markers that can be used to differentiate between patients with PD and MCI and patients with PD without MCI. © RSNA, 2016 Online supplemental material is available for this article.

Objective: To use a multimodal approach to assess brain structural pathways and resting state (RS) functional connectivity abnormalities in patients with Parkinson's disease and freezing of gait (PD-FoG)., Methods: T1-weighted, diffusion tensor (DT) MRI and RS functional MRI (fMRI) were obtained from 22 PD-FoG patients and 35 controls on a 3.0 T MR scanner. Patients underwent clinical, motor, and neuropsychological evaluations. Gray matter (GM) volumes and white matter (WM) damage were assessed using voxel based morphometry and tract-based spatial statistics, respectively. The pedunculopontine tract (PPT) was studied using tractography. RS fMRI data were analyzed using a model free approach investigating the main sensorimotor and cognitive brain networks. Multiple regression models were performed to assess the relationships between structural, functional, and clinical/cognitive variables. Analysis of GM and WM structural abnormalities was replicated in an independent sample including 28 PD-FoG patients, 25 PD patients without FoG, and 30 healthy controls who performed MRI scans on a 1.5 T scanner., Results: Compared with controls, no GM atrophy was found in PD-FoG cases. PD-FoG patients showed WM damage of the PPT, corpus callosum, corticospinal tract, cingulum, superior longitudinal fasciculus, and WM underneath the primary motor, premotor, prefrontal, orbitofrontal, and inferior parietal cortices, bilaterally. In PD-FoG, right PTT damage was associated with a greater disease severity. Analysis on the independent PD sample showed similar findings in PD-FoG patients relative to controls as well as WM damage of the genu and body of the corpus callosum and right parietal WM in PD-FoG relative to PD no-FoG patients. RS fMRI analysis showed that PD-FoG is associated with a decreased functional connectivity of the primary motor cortex and supplementary motor area bilaterally in the sensorimotor network, frontoparietal regions in the default mode network, and occipital cortex in the visual associative network., Conclusions: This study suggests that FoG in PD can be the result of a poor structural and functional integration between motor and extramotor (cognitive) neural systems., (© 2015 Wiley Periodicals, Inc.)