19 research outputs found

    Mapping holmes tremor circuit using the human brain connectome

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    ObjectiveHolmes tremor is a debilitating movement disorder with limited treatment options. Lesions causing Holmes tremor can occur in multiple different brain locations, leaving the neuroanatomical substrate unclear. Here, we test whether lesion locations that cause Holmes tremor map to a connected brain circuit and whether this circuit might serve as a useful therapeutic target.MethodsCase reports of Holmes tremor caused by focal brain lesions were identified through a systematic literature search. Connectivity between each lesion location and the rest of the brain was computed using resting state functional connectivity magnetic resonance imaging data from 1,000 healthy volunteers. Commonalities across lesion locations were identified. This Holmes tremor circuit was then compared to neurosurgical treatment targets and clinical efficacy.ResultsWe identified 36 lesions causing Holmes tremor, which were scattered across multiple different brain regions. However, all lesion locations were connected to a common brain circuit with nodes in the red nucleus, thalamus, globus pallidus, and cerebellum. In cases with effective neurosurgical treatment, the treatment target was connected with the lesion location, indicating that a second hit to the same circuit might be beneficial. Commonly used deep brain stimulation targets such as the ventral intermediate nucleus and subthalamic nucleus fell outside our Holmes tremor circuit, whereas the globus pallidus target was close, consistent with published clinical response rates for these targets.InterpretationLesions causing Holmes tremor are part of a single connected brain circuit that may serve as an improved therapeutic target.</p

    Comparison of VIM and STN DBS for Parkinsonian Resting and Postural/Action Tremor

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    Background: Resting tremor is common in Parkinson’s disease (PD), but up to 47% of PD patients have action tremor, which is sometimes resistant to medications. Deep brain stimulation (DBS) of the ventral intermediate nucleus (VIM) of the thalamus or subthalamic nucleus (STN) is effective for medication-refractory tremor in PD, though it remains unclear whether STN DBS is as effective as VIM DBS for postural/action tremor related to PD. Methods: We carried out a single-center retrospective review of patients with medication-refractory resting, postural, and action PD tremor, treated with either VIM or STN DBS between August 2004 and March 2014. We assessed the degree of improvement using items 20 and 21 of the Unified Parkinson’s Disease Rating Scale (UPDRS) motor scale and examined the proportion of patients achieving tremor arrest. Results: A total of 18 patients were analyzed, 10 treated with STN and eight treated with VIM, with similar off-medication motor UPDRS scores. There was no significant difference in improvement in tremor scores or in the proportion of patients experiencing tremor arrest between the two stimulation sites. Overall, 56% and 72% of patients experienced complete absence of postural/action tremor and resting tremor, respectively, at last follow-up. Discussion This study demonstrated excellent outcomes on both resting and postural/action tremor after either VIM or STN DBS. Resting tremor improved to a greater degree than postural/action tremor in both groups. These results suggest that a large randomized controlled trial is needed to show a superior effect of one target on PD tremor

    Perfusion-weighted magnetic resonance imaging thresholds identifying core, irreversibly infarcted tissue.

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    BACKGROUND AND PURPOSE: Identifying core, irreversibly infarcted tissue and salvageable penumbral tissue is crucial to informed, physiologically guided decision making regarding thrombolytic and other interventional therapies in acute ischemic stroke. Pretreatment perfusion MRI offers promise as a means to differentiate core from penumbral tissues. METHODS: Diffusion-perfusion MRIs were performed before treatment and on day 7 in patients undergoing successful vessel recanalization with intra-arterial thrombolytic therapy. Perfusion maps of the time to peak of the residue function (Tmax) were generated after deconvolution of an arterial input function. Initial perfusion abnormalities and final infarct regions were outlined by hand. Posttreatment images were coregistered to the pretreatment study. Voxel-by-voxel and volume analyses were performed to identify thresholds of perfusion abnormalities that best predict core, irreversibly infarcted tissue. RESULTS: Fourteen patients (4 men, 10 women) with vessel recanalization were studied. Mean age was 73 years, and median entry National Institutes of Health Stroke Scale score was 12. Mean time from symptom onset to start of intra-arterial infusion was 245 minutes and to recanalization was 338 minutes. With a voxel-by-voxel analysis, Tmax \u3e or =6 and \u3e or =8 seconds (sensitivity, 71% and 53%; specificity, 63% and 80%) correlated most highly with day 7 final infarct. With a volume analysis, Tmax \u3e or =6 and \u3e or =8 seconds (r2=0.704 and r2=0.705) correlated most highly with day 7 final infarct. CONCLUSIONS: Perfusion-weighted imaging measures of ischemia severity accurately differentiate irreversibly injured core from penumbral, salvageable tissue. The best threshold for identifying core infarcted tissue is adjusted Tmax of \u3e or =6 to 8 seconds

    Design and Feasibility Analysis of a Smartphone‐Based Digital Cognitive Assessment Study in the Framingham Heart Study

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    Background Smartphone‐based digital technology is increasingly being recognized as a cost‐effective, scalable, and noninvasive method of collecting longitudinal cognitive and behavioral data. Accordingly, a state‐of‐the‐art 3‐year longitudinal project focused on collecting multimodal digital data for early detection of cognitive impairment was developed. Methods and Results A smartphone application collected 2 modalities of cognitive data, digital voice and screen‐based behaviors, from the FHS (Framingham Heart Study) multigenerational Generation 2 (Gen 2) and Generation 3 (Gen 3) cohorts. To understand the feasibility of conducting a smartphone‐based study, participants completed a series of questions about their smartphone and app use, as well as sensory and environmental factors that they encountered while completing the tasks on the app. Baseline data collected to date were from 537 participants (mean age=66.6 years, SD=7.0; 58.47% female). Across the younger participants from the Gen 3 cohort (n=455; mean age=60.8 years, SD=8.2; 59.12% female) and older participants from the Gen 2 cohort (n=82; mean age=74.2 years, SD=5.8; 54.88% female), an average of 76% participants agreed or strongly agreed that they felt confident about using the app, 77% on average agreed or strongly agreed that they were able to use the app on their own, and 81% on average rated the app as easy to use. Conclusions Based on participant ratings, the study findings are promising. At baseline, the majority of participants are able to complete the app‐related tasks, follow the instructions, and encounter minimal barriers to completing the tasks independently. These data provide evidence that designing and collecting smartphone application data in an unsupervised, remote, and naturalistic setting in a large, community‐based population is feasible

    Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research

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    ABSTRACT Most research using digital technologies builds on existing methods for staff‐administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home‐based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early‐stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow‐up possible. Through the American Heart Association–funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations
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