1. TRANSCRANIAL MAGNETIC STIMULATION (TMS) TO EVALUATE AND CLASSIFY MENTAL DISEASE USING NEURAL NETWORK Dipartimento di Ingegneria Informatica e delle Telecomunicazioni, e Dipartimento di Neuroscienze dell’Universita’ di Catania Italy A. Faro, D. Giordano, M. Pennisi, G. Scarciofalo, C. Spampinato, F. Tramontana

2. Aim of the work to propose a neural network methodology able to process the signals related to the hands movements in response to the Transcranial Magnetic Stimulation (TMS) for diagnosing the pathology and evaluating the therapy to treat the patients affected by demency diseases such as Alzheimer and Subcortical Ischemic Vascular Dementia (SIVD).

3. TRANSCRANIAL MAGNETIC STIMULATION The Transcranial Magnetic Stimulation (TMS) produces a modification of the neuronal activity related to a defined brain area. Such a modification determines some involuntary movements of the legs and of the hands depending on the brain area influenced by the variable magnetic field generated by a coil put in proximity of the head.

4. Magnetic Stimulation Hand movement (Response) Amplitude of the response Latency Electro Myography (EMG) Inter-stimulus Interval time in milliseconds TRANSCRANIAL MAGNETIC STIMULATION

5. Magnetic Stimulation Hand movement (Response) Amplitude of the response Latency Electro Myography (EMG) Inter-stimulus Interval time in milliseconds TRANSCRANIAL MAGNETIC STIMULATION CIRCULAR COIL

6. Magnetic Stimulation Hand movement (Response) Amplitude of the response Latency Electro Myography (EMG) Inter-stimulus Interval time in milliseconds TRANSCRANIAL MAGNETIC STIMULATION MAGNETIC FIELD GENERATED BY CIRCULAR COIL

7. PROTOCOL The PROTOCOL of the tests is as follows: “Bi-Stim-Before” - two stimulations are given to the subject; the first acts as a conditioning stimulus, the second acts as the testing stimulus. The muscular responses to be compared with the ones obtained after the repetitive stimulation are taken and stored by the EMG. “R-Stim” - a repetitive stimulation is administered to the subject for a certain period of time (e.g., one repetitive stimulation session per day for 15 days). The patient is stimulated for about 30 minutes by a magnetic field at a stimulation frequency between 1 Hz and 30 Hz depending on the pathology. “Bi-Stim-After” - the patient is stimulated as in phase “Bi- Stim-Before”

8. RELEVANT VARIABLES IDENTIFICATION Several variables, defined on the basis of the signals detected by EMG, have been analyzed. The relevant variables are the ones whose value significantly changes before and after rTMS.

9. RELEVANT VARIABLES IDENTIFICATION

10. NEURAL NETWORK BASED DIAGNOSIS SIVD Mixed dementia Alzheimer None

11. NEURAL NETWORK BASED DIAGNOSIS SIVD Mixed demntia Alzheimer None The difference between the values of the relevant variables with respect to the same ISI before and after r-Stim are given as inputs to a neural net to evaluate if the patient is affected by Alzheimer, SIVD or if she/he is in healthy condition. The net has been trained with 14 patients and has been tested with 8 patients. The diagnosis was successful in about 90% of cases.

12. A TMS ANALYSER HAS BEEN DEVELOPED

13. TMS ANALYSER It allows us to: - Visualize the signals defined by the user before and after r-Stim in order to identify the relevant variables - Process the relevant variables using Neural Network and Fuzzy Logic

14. CONCLUSIONS AND FUTURE WORK With respect to a fuzzy classifier previously proposed by the authors, the neural network classifier has a better performance to diagnose crisp cases (i.e. no mental disease, SIVD, Alzheimer). The fuzzy classifier is more suitable to measure the mental disease degree. An integration of the two classification methods could be very effective. To increase precision to detect the various forms of dementia we plan to increase the number of the inputs of the neural network or of the fuzzy classifier by taking into account variables related to other experiments and practices.