Emerging Technologies That Will Revolutionize Neurological ... · Journal of Stroke and...

27 settembre 2018

Emerging Technologies That Will Revolutionize Neurological Care

Prof. Paolo Manganotti

Neurology Clinic of Trieste University Hospital

Emerging Technologies That Will Revolutionize Neurological Care

Neuroimaging for Neurodegenerative Disorders and Stoke

Early Diagnosis and Treatment of Alzheimer’s Disease

The Promise of Brain and genetic Biomarkers

Healing the Brain with Neuromodulation

Innovative Pharmacological and genetic Therapy

Robotics for Neuro Rehabilitation

Digital health and sensors for Acute and Chronic Care

Neuroimaging biomarkers, correlation with CFS biomarkers and joint analysis with high density EEG in early diagnosis

and prognosis of Dementias

3T MRI

Perfusion MRI high density EEG

ASL

Ischemic Volume and Neurological Deficit: Correlation ofCT Perfusion with the NIHSS Score in Acute Ischemic Stroke

G. Furlanis, M. Ajčević ,P. Maganotti -Journal of Stroke and Cerebrovascular Diseases, 2018

Brain Oscillatory Activity and CT Perfusion in Hyper-Acute

Ischemic Stroke

L. Stragapede, M. Ajčević ,P. Maganotti - Journal of Stroke and Cerebrovascular Diseases, 2018

MeMoRi NET

Network for Mental Rehabilitation and Motors of the Ictus

The MEMORI-net project is a joint effort to improve rehabilitation strategies for patients who have suffered a stroke

Rehabilitation and Cognitive performance with app

Neurophysiological mechanisms underlying underwater Parkinson’s disease rehabilitation

……..

Robotic bed for acute neurological rehabilitation-BTS

EEG findings

Topographic maps showing ERD and t values. Grand average maps of ERD/ERS in alpha andbeta bands during immagination of movement. Blue color coding indicates maximal ERD. T-mapsof ERD/ERS in alpha and beta bands thresholded at p<0.005

Event-related power decrease during motor imagery task in alpha and beta was bilateral, localizedover both ipsilateral and contralateral motor cortical region.

High density EEG system

EEG cap with 256 channels

(Electrical Geodesics Inc. Eugene,OR, USA)

Elastic tension structure and electrolyte solution

Ag/AgCl electrodes

Application time of 10-15 minutes

Rate of acquisition (until 20 kHz)

- Elettrodo in fibra di carbonio

- Utilizzazione soluzione acquosa e potassio

- Funzionamento ad alta impedenza

- Amplificatori a 32 – 64 - 128 – 256 canali

dedicati EGI

Geodesic Sensor Net

- Utilizzazione soluzione conduttiva acqua e potassio

- Spugna SuperDry ad elevata capacità di assorbimenteo acqua

- Microclima di mantenimento umidità che sfrutta il calore corporeo; cica 2 ore

Geodesic Sensor Net

Multimodality approach

High density EEG 256 channels Anatomical MRI 3T

Time course of the EEG sourceRising phase

Time course of the EEG sourcePeak

fMRI measures the hemodynamic response related to neural activity in the brain. BOLD signal (Blood Oxygenation Level Dependent)

Standard EEG30 channels acquiredduring fMRI

EEG misures neuronal currents from the scalp with high temporal resolution (ms) but limited number of EEG channels

• fMRI: high spatial resolution• EEG: high temporal resolution

EEG-fMRI coregistration system

EEG-fMRI coregistration

EEG-fMRI coregistration system

EEG during fMRI EEG filtered

Regressor

fMRI map

EEG-fMRI: conventional analysis in epilepsy

Artifact subctraction

(Allen et al., 2000)

GLM

(Friston et al.,1995)

Mostra Desktop.scf

HRF

Visual detection

Reproducibility of EEG-fMRI results: overlapping regions were localized in thesame Brodmann areas with 1762 common voxels in area 40 and in area 21.

•Synchronization of Neuronal Activity in the Human Primary Motor Cortex

•by Transcranial magnetic stimulation: An EEG Study. Paus et al. 2001

Hd EEG and TMS

Alternative splicing as a potential biomarker for Parkinson’s

disease

Valentina Tommasini

Prof. Paolo Manganotti

Prof. Emanuele Buratti

Prof. Maurizio Romano

Dott. Mauro Catalan

UNIVERSITA’ DEGLI STUDI DI TRIESTE

DIPARTIMENTO DI SCIENZE MEDICHE, CHIRURGICHE E

DELLA SALUTE

a-sinucleina

La Malattia di Parkinson è una sinucleinopatia

Obiettivo dello studio

Identificare biomarcatori nel sangue dei pazienti affetti da Malattia di Parkinson

Ricercare variazioni dello splicing alternativo nell’RNA leucocitario

Risultati

2) Espressione genica

SNCA (alfa-

sinucleina)

Principale componente dei corpi di

Lewy

LRRK2(dardarina)

Causa più frequente di PD

autosomico dominante e

fattore di rischio per PD sporadico

PARK2(parkina)

50% dei PD autosomici

recessivi

Risultati

3) Splicing alternativo

ATXN2(atassina-2)

Responsabile della SCA-2 e

fattore di rischio per SLA

e PSP

HSPH1(heat-shock protein 1)

Folding proteico

LRRFIP1(leucine-rich repeat flightless-interacting

protein 1)

Risposta allo stress cellulare

Henderson-Smith, A. et al. Next-generation profiling to identify the molecular etiology of Parkinson dementia. Neurol Genet (2016).

Linee di sviluppo applicative in Neurologia CLINICA

DEVICE WIRELSS E SENSORI IN FASE ACUTA E DOMOTICA

DEVICE ROBOTICI E NEURO-PROTESI

BIOIMAGING E NEUROFISIOLOGIA

GENETICA – BIOMARKERS – FARMOGENETICA