Using machine learning and accelerometry data for differential diagnosis of Parkinson’s disease and essential tremor

Parkinson’s disease (PD) and Essential Tremor (ET) are the most common tremor syndromes in the world. Currently, a specific Single Photon Emission Computed Tomography (123I-FP-CIT SPECT) has proven to be an effective tool for the diagnosis of these diseases (97% sensitivity and 100% specificity). However, this test is invasive and expensive, and not all countries can have a SPECT system for an accurate differential diagnosis of PD patients. Clinical evaluation by a neurologist remains the gold standard for PD diagnosis, although the accuracy of this protocol depends on the experience and expertise of the physician. Wearable devices have been found to be a potential tool to help in differential diagnosis of PD and ET in early or complex cases. In this paper, we analyze the linear acceleration of the hand tremor recorded with a built-in accelerometer of a mobile phone, with a sampling frequency of 100 Hz. This hand tremor signal was thoroughly analyzed to extract different kinematic features in the frequency domain. These features were used to explore different Machine Learning methods to automatically classify and differentiate between healthy subjects and hand tremor patients (HETR Group) and, subsequently, patients with PD and ET (ETPD Group). Sensitivity of 90.0% and Specificity of 100.0% were obtained with classifiers of the HETR group. On the other hand, classifiers with Sensitivity ranges from 90.0% to 100.0% and Specificity from 80% to 100% were obtained for the ETPD group. These results indicate that the method proposed can be a potential tool to help the clinicians on differential diagnosis in complex or early hand tremor casesPeer ReviewedPostprint (published version

Myoclonus-dystonia: significance of large SGCE deletions.

Myoclonus-dystonia (M-D) is an autosomal-dominant movement disorder caused by mutations in SGCE. We investigated the frequency and type of SGCE mutations with emphasis on gene dosage alterations and explored the associated phenotypes. We tested 35 M-D index patients by multiplex ligation-dependent probe amplification (MLPA) and genomic sequencing. Mutations were found in 26% (9/35) of the cases, all but three with definite M-D. Two heterozygous deletions of the entire SGCE gene and flanking DNA and a heterozygous deletion of exon 2 only were detected, accounting for 33% (3/9) of the mutations found. Both large deletions contained COL1A2 and were additionally associated with joint problems. Further, we discovered one novel small deletion (c.771_772delAT, p.C258X) and four recurrent point mutations (c.289C>T, p.R97X; c.304C>T, p.R102X; c.709C>T, p.R237X; c.1114C>T, p.R372X). A Medline search identified 22 articles on SGCE mutational screening. Sixty-four unrelated M-D patients were described with 41 different mutations. No genotype-phenotype association was found, except in patients with deletions encompassing additional genes. In conclusion, a rigorous clinical preselection of patients and careful accounting for non-motor signs should precede mutational tests. Gene dosage studies should be included in routine SGCE genetic testing

The pathophysiology of essential tremor and Parkinson's tremor

Item does not contain fulltextWe review recent evidence about the pathophysiology of essential tremor and tremor in Parkinson’s disease. We believe that a network perspective is necessary to understand this common neurological symptom, and that knowledge of cerebral network dysfunction in tremor disorders will help to develop new therapies. Both essential tremor and Parkinson’s tremor are associated with increased activity in the cerebellothalamocortical circuit. However, different pathophysiological mechanisms lead to tremulous activity within this circuit. In Parkinson’s disease, evidence suggests that dopaminergic dysfunction of the pallidum triggers increased activity in the cerebellothalamocortical circuit. In essential tremor, GABAergic dysfunction of the cerebellar dentate nucleus and brain stem, possibly caused by neurodegeneration in these regions, may lead to tremulous activity within the cerebellothalamocortical circuit. In both disorders, network parameters such as the strength and directionality of interregional coupling are crucially altered. Exciting new research uses these network parameters to develop network-based therapies, such as closed-loop deep brain stimulation and transcranial magnetic or direct current stimulation.10 p