Faculty Bibliography

Introduction: Rehabilitation training after stroke commonly focuses on practicing activities of daily living (ADLs), comprised of functional movements and, more fundamentally, functional primitives. Animal models have demonstrated extensive motor recovery if many functional movements are trained early after stroke. In humans, the optimal rehabilitation dose to maximize recovery is not known, in part because a tool to precisely but pragmatically measure rehabilitation does not currently exist. We are building a measurement tool that can objectively decompose ADLs into their constituent primitives. We report here developments in the first important step of building this tool-the automatic identification of functional primitives that constitute various ADLs.
Method(s): 32 stroke subjects (gender: 18F/14M; paretic side: 14R/18L; age: 56.2 +/- 13.54 years; time since stroke: 6.7 +/- 7.57 years; mean FuglMeyer score: 44.21 +/- 14.26) performed a battery of 9 ADLs in an inpatient gym. Participants wore 9 inertial measurement units (IMUs) on their cervical spine, thoracic spine, pelvis, and bilateral hands, forearms, and arms. The IMU system generated linear accelerations, orientations, quaternions, and joint angles at 100 Hz. Human coders used synchronously recorded video to segment each activity into its constituent primitives: reach, transport, stabilize, reposition, and idle. This segmentation step also assigned primitive labels to the IMU data. Using labeled IMU data, we trained a sequence-to-sequence convolutional neural network (CNN) in 21 subjects and tested it in 11 subjects. Subjects were chosen randomly and were balanced for paretic side. The model had 14 convolutional layers with batch normalization between each layer to reduce the covariate shift. Data windows of 1 s (with a slide of 0.25 s) were fed into the CNN. Using a softmax activation function, the final layer of the CNN generated the probability of the data sample being each primitive. The winning probability was chosen as the label name. To measure the classification accuracy (positive predictive value, PPV) of the approach, we compared the CNNgenerated label against the human-generated label for all data windows.
Result(s): Our approach had an average classification accuracy of 64% for identifying the five primitives. Its lowest accuracy was in identifying reaches (PPV 37%), which were commonly confused with transports. It was moderately accurate in identifying repositions (PPV 46%), which were also confused with transports. The approach performed well in identifying idles (PPV 67%), stabilizations (PPV 62%), and transports (PPV 60%).
Discussion(s): We present a novel approach for classifying functional primitives embedded in ADLs, an important step toward dose quantitation in rehabilitation. Though classification performance was modest, the approach performs well above chance (PPV 20%), affirming its plausibility for use in stroke patients. Future work will test other deep network architectures and data augmentation techniques to improve classification performance

Objectives: Patient satisfaction is one determinant of quality health care (Kondziolka et al. 2013). The performance of surgical procedures on conscious patients dictates unique considerations of patient comfort, experience, and satisfaction. In this study, we sought to better understand patients' experience during each stage of deep brain stimulation (

It is often unclear to the clinical investigator whether observational studies should be submitted to a research ethics committee (REC), mostly because, in general, no active or additional interventions are performed. Moreover, obtaining an informed consent under these circumstances may be challenging, either because these are very large epidemiological registries, or the subject may no longer be alive, is too ill to consent, or is impossible to contact after being discharged. Although observational studies do not involve interventions, they entail ethical concerns, including threats such as breaches in confidentiality and autonomy, and respect for basic rights of the research subjects according to the good clinical practices. In this context, in addition to their main function as evaluators from an ethical, methodological, and regulatory point of view, the RECs serve as mediators between the research subjects, looking after their basic rights, and the investigator or institution, safeguarding them from both legal and unethical perils that the investigation could engage, by ensuring that all procedures are performed following the international standards of care for research. The aim of this manuscript is to provide information on each type of study and its risks, along with actions to prevent such risks, and the function of RECs in each type of study.

Gradient-based approaches to brain function have recently unmasked fundamental properties of brain organization. Diffusion map embedding analysis of resting-state fMRI data revealed a primary-to-transmodal axis of cerebral cortical macroscale functional organization. The same method was recently used to analyze resting-state data within the cerebellum, revealing for the first time a sensorimotor-fugal macroscale organization principle of cerebellar function. Cerebellar gradient 1 extended from motor to non-motor task-unfocused (default-mode network) areas, and cerebellar gradient 2 isolated task-focused processing regions. Here we present a freely available and easily accessible tool that applies this new knowledge to the topographical interpretation of cerebellar neuroimaging findings. LittleBrain illustrates the relationship between cerebellar data (e.g., volumetric patient study clusters, task activation maps, etc.) and cerebellar gradients 1 and 2. Specifically, LittleBrain plots all voxels of the cerebellum in a two-dimensional scatterplot, with each axis corresponding to one of the two principal functional gradients of the cerebellum, and indicates the position of cerebellar neuroimaging data within these two dimensions. This novel method of data mapping provides alternative, gradual visualizations that complement discrete parcellation maps of cerebellar functional neuroanatomy. We present application examples to show that LittleBrain can also capture subtle, progressive aspects of cerebellar functional neuroanatomy that would be difficult to visualize using conventional mapping techniques. Download and use instructions can be found at https://xaviergp.github.io/littlebrain.

Alzheimer's disease (AD) is the most prevalent form of dementia. Cerebrovascular dysfunction is one of the earliest events in the pathogenesis of AD, as well as in vascular and mixed dementias. Cerebral amyloid angiopathy (CAA), the deposition of amyloid around cerebral vessels, is observed in up to 90% of AD patients and in approximately 50% of elderly individuals over 80 years of age. CAA is a strong contributor to vascular dysfunction in AD. CAA-laden brain vessels are characterized by dysfunctional hemodynamics and leaky blood-brain barrier (BBB), contributing to clearance failure and further accumulation of amyloid-β (Aβ) in the cerebrovasculature and brain parenchyma. Mitochondrial dysfunction is increasingly recognized as an important early initiator of the pathogenesis of AD and CAA. The objective of this review is to discuss the effects of Aβ on cerebral microvascular cell function, focusing on its impact on endothelial mitochondria. After introducing CAA and its etiology and genetic risk factors, we describe the pathological relationship between cerebrovascular amyloidosis and brain microvascular endothelial cell dysfunction, critically analyzing its roles in disease progression, hypoperfusion, and BBB integrity. Then, we focus on discussing the effect of Aβ challenge on endothelial mitochondrial dysfunction pathways, and their contribution to the progression of neurovascular dysfunction in AD and dementia. Finally, we report potential pharmacological and non-pharmacological mitochondria-targeted therapeutic strategies which may help prevent or delay cerebrovascular failure.