Monitoring the effects of sleep deprivation using neuronal avalanches

Abstract

The present invention is directed to a method of continuously monitoring neuronal avalanches in a subject comprising (a) determining a deviation in avalanche exponent (α) or branching parameter (σ) from a predetermined value at rest, wherein the pre-determined value of a is a slope of a size distribution of the synchronized neuronal activity and the predetermined value is −3/2 and the pre-determined value of 6 is a ratio of successively propagated synchronized neuronal activity and the predetermined value is 1; and (b) repeating step (a) one or more times to continuously monitor neuronal avalanches in a subject. The invention also features methods of determining or monitoring the degree of sleep deprivation in a subject, methods of identifying subjects that are susceptible to a sleep disorder and methods of diagnosing a sleep disorder in a subject.

What is claimed is: 
     
         1 . A method of identifying a subject that is susceptible to a sleep disorder comprising:
 (a) performing an EEG using sensors provided on the subject;   (b) determining, by at least one processing device, an avalanche exponent a or a branching parameter σ from signals output by the sensors, wherein α is a slope of a size distribution of a synchronized neuronal activity and σ is a ratio of successively propagated synchronized neuronal activity;   (c) determining, by the at least one processing device, a deviation in α or σ as determined from a pre-determined value of α or σ at rest, wherein:
 the pre-determined value of α is −3/2, and 
 the pre-determined value of σ is 1; 
   (d) repeating steps (a)-(c) one or more times;   (e) identifying, by the at least one processing devices, the subject as being susceptible to a sleeping disorder when it is determined there is a deviation in α or σ from the pre-determined value of α or σ;   (f) providing, by the at least one processing devices, a notification that the subject is identified as being susceptible to the sleeping disorder; and   (g) providing intervention or guidance of treatment to the subject in response to identification of the subject as being susceptible to the sleeping disorder.   
     
     
         2 . The method of  claim 1 , further comprising:
 (h) identifying the time when α and σ are determined to deviate from their pre-determined values at rest.   
     
     
         3 . The method of  claim 1 , wherein step (a) further comprises:
 (i) filtering the signals output from the sensors;   (ii) the sensors include multiple EEG electrodes, and positive/negative threshold crossings at each EEG electrode are detected;   (iii) clustering threshold crossings on the EEG electrodes on a pre-determined time scale; and   (iv) calculating the cluster size distribution for determining α or calculating a ratio of successive threshold crossings for determining σ.   
     
     
         4 . The method of  claim 3 , wherein the EEG is continuously recorded at more than one site. 
     
     
         5 . The method of  claim 3 , wherein the EEG is filtered between 1-100 Hz or wherein the time scale is 1-50 ms. 
     
     
         6 . The method of  claim 1 , wherein the subject with the deviation in α or σ from the pre-determined value of α or σ performs a psychomotor vigilance task. 
     
     
         7 . The method of  claim 1 , further comprising determining, by the at least one processing device, a magnitude and spatial distribution of theta power in the signals output by the sensors. 
     
     
         8 . The method of  claim 1 , further comprising gathering data from other physiological sensors. 
     
     
         9 . The method of  claim 1 , wherein the method is operational with hardware or software or a combination thereof. 
     
     
         10 . The method of  claim 1 , wherein the sensors include multiple dry electrodes of a headset, wherein the dry electrodes are individually isolated and amplified, wherein the headset is wearable in a non-laboratory setting. 
     
     
         11 . A method of diagnosing a sleep disorder in a subject comprising:
 (a) performing an EEG using sensors provided on the subject   (b) determining, by at least one processing device, an avalanche exponent α or a branching parameter σ from signals output by the sensors, wherein α is a slope of a size distribution of a synchronized neuronal activity and σ is a ratio of successively propagated synchronized neuronal activity;   (c) determining, by the at least one processing device, a deviation in α or σ as determined from a pre-determined value of α or σ at rest, wherein:   the pre-determined value of α is −3/2, and   the pre-determined value of δ is 1;   (d) repeating step (a)-(c) one or more times;   (e) diagnosing, by the at least one processing device, the subject as having a sleeping disorder when it is determined there is a deviation in α or σ from the pre-determined value of α or σ;   (f) providing, by the at least one processing devices, a notification that the subject is diagnosed as having the sleeping disorder; and   (g) providing intervention or guidance of treatment to the subject in response to diagnosing the subject as having the sleeping disorder.   
     
     
         12 . The method of  claim 11 , further comprising:
 (h) identifying the time when α and σ are determined to deviate from their pre-determined values at rest.   
     
     
         13 . The method of  claim 11 , wherein step (a) further comprises:
 (i) filtering the signals output from the sensors;   (ii) the sensors include multiple EEG electrodes, and positive/negative threshold crossings at each EEG electrode are detected;   (iii) clustering threshold crossings on the EEG electrodes on a pre-determined time scale;   and (iv) calculating the cluster size distribution for determining a or calculating a ratio of successive threshold crossings for determining σ.   
     
     
         14 . The method of  claim 13 , wherein the EEG is continuously recorded at more than one site. 
     
     
         15 . The method of  claim 13 , wherein the EEG is filtered between 1-100 Hz or wherein the time scale is 1-50 ms. 
     
     
         16 . The method of  claim 11 , wherein the subject with the deviation in α or σ from the pre-determined value of α or σ performs a psychomotor vigilance task. 
     
     
         17 . The method of  claim 11 , further comprising determining, by the at least one processing device, a magnitude and spatial distribution of theta power in the signals output by the sensors. 
     
     
         18 . The method of  claim 11 , further comprising gathering data from other physiological sensors. 
     
     
         19 . The method of  claim 11 , wherein the method is operational with hardware or software or a combination thereof. 
     
     
         20 . The method of  claim 11 , wherein the sensors include multiple dry electrodes of a headset, wherein the dry electrodes are individually isolated and amplified, wherein the headset is wearable in a non-laboratory setting.