Aphasia - Identifying Cognitive Decline - Revision history

Usha at 17:40, 30 May 2022

2022-05-30T17:40:01Z

Usha: /* Abstract */

2022-05-30T17:38:03Z

‎Abstract

Usha at 17:28, 30 May 2022

2022-05-30T17:28:31Z

Usha: /* Abstract */

2022-05-30T17:22:48Z

‎Abstract

Usha at 17:21, 30 May 2022

2022-05-30T17:21:59Z

Usha: /* Abstract */

2022-05-30T17:20:57Z

‎Abstract

Usha at 17:09, 30 May 2022

2022-05-30T17:09:31Z

Usha: /* Overview */

2022-05-30T17:03:23Z

‎Overview

Usha: /* Abstract */

2022-05-30T17:02:44Z

‎Abstract

Usha at 15:15, 30 May 2022

2022-05-30T15:15:43Z

← Older revision		Revision as of 17:40, 30 May 2022
Line 42:		Line 42:
	# Create models of language production that predict changes in cognition, language, and brain health.		# Create models of language production that predict changes in cognition, language, and brain health.
	[[Image:Wiki Figure 1.jpg \| center \| 400px \| thumb \| Discourse Transcript - NLP Feature Extraction]]		[[Image:Wiki Figure 1.jpg \| center \| 400px \| thumb \| Discourse Transcript - NLP Feature Extraction]]
		+	=Publications=
		+	[https://www.sciencedirect.com/science/article/pii/S2666956021000064 Newman-Norlund, R. D., Newman-Norlund, S. E., Sayers, S., Nemati, S., Riccardi, N., Rorden, C., & Fridriksson, J. (2021). The Aging Brain Cohort (ABC) repository: The University of South Carolina’s multimodal lifespan database for studying the relationship between the brain, cognition, genetics and behavior in healthy aging. Neuroimage: Reports, 1(1), 100008.]

@@ Line 26: / Line 26: @@
 [[Image:Wiki Figure 1.jpg | center | 400px | thumb | Aphasia Study Pipeline]]
-'''Results:''' Data from the ABC@UofSC Repository are easily accessible upon request (abc.sc.edu), and
+'''Results:'''
-our publicly available statistics and visualization tools provide collaborating researchers with the ability to
+The Aphasia study pipeline helps in automating the NLP (Natural Language Processing) feature extraction, Main concept analysis, MoCA score assessment, predicting cognitive impairment, and explainable predictions.
-identify associations between brain structure and function in relation to genetic variation and behavioral mea-
-sures across the adult lifespan with unprecedented ease and rapidity.
 '''Conclusions:'''
-The Aphasia study pipeline helps in automating the NLP (Natural Language Processing) feature extraction, Main concept analysis, MoCA score assessment, predicting cognitive impairment, and explainable predictions.
+Data from the ABC@UofSC Repository are easily accessible upon request (abc.sc.edu), and
 =Overview=

← Older revision		Revision as of 17:28, 30 May 2022
Line 24:		Line 24:
	is automated to assist the clinician to make informed decisions. This automatic pipeline (Figure 2) includes extracting language features, and speech features, building regression models to predict MoCA scores, and classification models to predict Cognitive impairment levels (mild, moderate, severe).		is automated to assist the clinician to make informed decisions. This automatic pipeline (Figure 2) includes extracting language features, and speech features, building regression models to predict MoCA scores, and classification models to predict Cognitive impairment levels (mild, moderate, severe).

−	~~[[File:Example.jpg]]~~
	[[Image:Wiki Figure 1.jpg \| center \| 400px \| thumb \| Aphasia Study Pipeline]]		[[Image:Wiki Figure 1.jpg \| center \| 400px \| thumb \| Aphasia Study Pipeline]]

@@ Line 24: / Line 24: @@
 is automated to assist the clinician to make informed decisions. This automatic pipeline (Figure 2) includes extracting language features, and speech features, building regression models to predict MoCA scores, and classification models to predict Cognitive impairment levels (mild, moderate, severe).
-[[File:Fig 1.jpg]]
+[[File:Example.jpg]]
 [[Image:Wiki Figure 1.jpg | center | 400px | thumb | Aphasia Study Pipeline]]

@@ Line 24: / Line 24: @@
 is automated to assist the clinician to make informed decisions. This automatic pipeline (Figure 2) includes extracting language features, and speech features, building regression models to predict MoCA scores, and classification models to predict Cognitive impairment levels (mild, moderate, severe).
-[[File:Example.jpg]]
+[[File:Fig 1.jpg]]
 [[Image:Wiki Figure 1.jpg | center | 400px | thumb | Aphasia Study Pipeline]]

← Older revision		Revision as of 17:20, 30 May 2022
Line 24:		Line 24:
	is automated to assist the clinician to make informed decisions. This automatic pipeline (Figure 2) includes extracting language features, and speech features, building regression models to predict MoCA scores, and classification models to predict Cognitive impairment levels (mild, moderate, severe).		is automated to assist the clinician to make informed decisions. This automatic pipeline (Figure 2) includes extracting language features, and speech features, building regression models to predict MoCA scores, and classification models to predict Cognitive impairment levels (mild, moderate, severe).

		+	[[File:Example.jpg]]
	[[Image:Wiki Figure 1.jpg \| center \| 400px \| thumb \| Aphasia Study Pipeline]]		[[Image:Wiki Figure 1.jpg \| center \| 400px \| thumb \| Aphasia Study Pipeline]]

@@ Line 22: / Line 22: @@
 social, emotional, cognitive, and language measures. Currently, data has been collected from 65 participants (ages
 –80) in the longitudinal arm of the project. The current manual intensive analysis of discourse transcripts
-is automated to assist the clinician to make informed decisions. This automatic pipeline (Figure 1) includes extracting language features, and speech features, building regression models to predict MoCA scores, and classification models to predict Cognitive impairment levels (mild, moderate, severe).
+is automated to assist the clinician to make informed decisions. This automatic pipeline (Figure 2) includes extracting language features, and speech features, building regression models to predict MoCA scores, and classification models to predict Cognitive impairment levels (mild, moderate, severe).
 '''Results:''' Data from the ABC@UofSC Repository are easily accessible upon request (abc.sc.edu), and
@@ Line 38: / Line 40: @@
 # Identify language factors related to brain health that can act as predictors of cognitive decline in healthy older adults.
 # Create models of language production that predict changes in cognition, language, and brain health.

@@ Line 35: / Line 35: @@
 '''Specific Aims:'''
-# Develop Aphasia study pipeline for automated processing of Language measures, Semantic, Syntactic, Lexical, and Speech features of discourse transcripts (Cat Rescue and Cookie Theft).
+# Develop Aphasia study pipeline for automated processing of Language measures, Semantic, Syntactic, Lexical, and Speech features of discourse transcripts (Cat Rescue and Cookie Theft) Figure 2.
 # Identify language factors related to brain health that can act as predictors of cognitive decline in healthy older adults.
 # Create models of language production that predict changes in cognition, language, and brain health.

@@ Line 10: / Line 10: @@
 =Abstract=
-'''Aims:''' The purpose of this work is to analyze cognitive decline characteristics of healthy older adults, describe the features in discourse transcripts, and identify factors in the speech to text analysis that contributed to the severity of cognitive impairment. These are the problems that Artificial Intelligence is solving in the Aphasia project: 1. Which aspects of spoken language can predict cognitive impairment and/or decline? 2. What is the relationship between cognition, language, and brain health in normal aging? 3. What language factors related to brain health can act as predictors of cognitive decline in healthy older adults? 4. How Natural Language Understanding can help create models of language production that may predict changes in cognition, language, and brain health?
+'''Aims:''' The purpose of this work is to analyze cognitive decline characteristics of healthy older adults, analyze the language features in discourse transcripts, and identify factors in the speech to text analysis that contributed to the severity of cognitive impairment. These are the problems that Artificial Intelligence is solving in the Aphasia project: 1. Which aspects of spoken language can predict cognitive impairment and/or decline? 2. What is the relationship between cognition, language, and brain health in normal aging? 3. What language factors related to brain health can act as predictors of cognitive decline in healthy older adults? 4. How Natural Language Understanding can help create models of language production that may predict changes in cognition, language, and brain health?
 '''Methods:'''
-The team created public repository that houses multimodal data collected as part of Aging Brain Cohort
+The team created a public repository that houses multimodal data collected as part of the Aging Brain Cohort
-study being conducted at the University of South Carolina (ABC@UofSC). Ultimately, the ABC@UofSC Repository
+the study being conducted at the University of South Carolina (ABC@UofSC). Ultimately, the ABC@UofSC Repository
-will contain diverse data from cross-sectional (N ¼ 800, age ¼ 20–80) and longitudinal (N ¼ 200, age ¼ 60–80,
+will contain diverse data from cross-sectional (N=800, age=20–80) and longitudinal (N=200, age=60–80,
-interesting interval ¼ 4 years) samples of healthy South Carolinians which include socio-demographic data, raw
+interesting interval=4 years) samples of healthy South Carolinians which include socio-demographic data, raw
 and preprocessed functional (resting-state and task-based fMRI, ASL) and structural (T1, T2 FLAIR, DWI, SWI)
 MRI data, raw, and preprocessed resting-state EEG, comprehensive blood work, measures of physical and sensory
 function, genetic data derived from whole blood and buccal swabs, and results from a unique constellation of
-social, emotional, cognitive and language measures. Currently, data has been collected from 65 participants (ages
+social, emotional, cognitive, and language measures. Currently, data has been collected from 65 participants (ages
-–80) in the longitudinal arm of the project.
+–80) in the longitudinal arm of the project. The current manual intensive analysis of discourse transcripts
 '''Results:''' Data from the ABC@UofSC Repository are easily accessible upon request (abc.sc.edu), and
 our publicly available statistics and visualization tools provide collaborating researchers with the ability to
 identify associations between brain structure and function in relation to genetic variation and behavioral mea-
-sures across the adult life-span with unprecedented ease and rapidity.
+sures across the adult lifespan with unprecedented ease and rapidity.
 '''Conclusions:'''

← Older revision		Revision as of 15:15, 30 May 2022
Line 30:		Line 30:
	'''Conclusions:'''		'''Conclusions:'''
	The Aphasia study pipeline helps in automating the NLP (Natural Language Processing) feature extraction, Main concept analysis, MoCA score assessment, predicting cognitive impairment, and explainable predictions.		The Aphasia study pipeline helps in automating the NLP (Natural Language Processing) feature extraction, Main concept analysis, MoCA score assessment, predicting cognitive impairment, and explainable predictions.
		+
		+	=Overview=
		+
		+	'''Specific Aims:'''
		+	# Develop Aphasia study pipeline for automated processing of Language measures, Semantic, Syntactic, Lexical, and Speech features of discourse transcripts (Cat Rescue and Cookie Theft).
		+	# Identify language factors related to brain health that can act as predictors of cognitive decline in healthy older adults.
		+	# Create models of language production that predict changes in cognition, language, and brain health.