Lisa Gabel | Lafayette College (original) (raw)

Papers by Lisa Gabel

ABSTRACT A brain-computer interface (BCI) relies on a classifier to determine a user's in... more ABSTRACT A brain-computer interface (BCI) relies on a classifier to determine a user's intent through the EEG signals. This classifier needs to be trained with a specific user prior to its usage. Since the effectiveness of a classifier is affected by the user's motivation during training, a memory game using the BCPy2000 platform has been developed for enhancing motivation and performance in using a traditional P300-based BCI system. A pilot study showed that this memory game is accomplishable by human subjects in a BCI system.

Developmental Neuroscience

Reading disability (RD), which affects between 5 and 17% of the population worldwide, is the most... more Reading disability (RD), which affects between 5 and 17% of the population worldwide, is the most prevalent form of learning disability, and is associated with underactivation of a universal reading network in children. However, recent research suggests there are differences in learning rates on cognitive predictors of reading performance, as well as differences in activation patterns within the reading neural network, based on orthographic depth (i.e., transparent/shallow vs. deep/opaque orthographies) in children with RD. Recently, we showed that native English-speaking children with RD exhibit impaired performance on a maze learning task that taps into the same neural networks that are activated during reading. In addition, we demonstrated that genetic risk for RD strengthens the relationship between reading impairment and maze learning performance. However, it is unclear whether the results from these studies can be broadly applied to children from other language orthographies. ...

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Brain-computer interface (BCI) devices are designed to bypass neuromuscular pathways enabling an ... more Brain-computer interface (BCI) devices are designed to bypass neuromuscular pathways enabling an individual to operate an external device using neural activity, as opposed to motor activity. Neural outputs are detected and utilized to drive a command signal to control a device, such as a wheelchair. BCIs provide individuals with severe motor impairment with an alternative mechanism to interact with their environment. Ultimately BCIs help to restore some degree of autonomy to an individual with limited motor control. Increased autonomy will likely lead to an enhanced quality of life of the individual as well as the caregiver(s). In this paper, we developed a BCI-controlled, ground-based robot. A wireless EEG headset was used to obtain the EEG signals from the user and stream the signals wirelessly into a host computer. The computer processed the EEG signals, extracted possible features from the signals to interpret the user's intents as the control commands, and then sent the command signals wirelessly to navigate the robot. This paper discusses the methodology used in the design as well as the response speed and accuracy in robot navigation from the test results. Future applications in assistive technology by using similar design concept are also discussed.

Developmental Neuroscience

Dyslexia is a common learning disability that affects processing of written language despite adeq... more Dyslexia is a common learning disability that affects processing of written language despite adequate intelligence and educational background. If learning disabilities remain untreated, a child may experience long-term social and emotional problems, which influence future success in all aspects of their life. Dyslexia has a 60% heritability rate, and genetic studies have identified multiple dyslexia susceptibility genes (DSGs). DSGs, such as DCDC2, are consistently associated with the risk and severity of reading disability (RD). Altered neural connectivity within temporoparietal regions of the brain is associated with specific variants of DSGs in individuals with RD. Genetically altering DSG expression in mice results in visual and auditory processing deficits as well as neurophysiological and neuroanatomical disruptions. Previously, we demonstrated that learning deficits associated with RD can be translated across species using virtual environments. In this 2-year longitudinal stu...

2022 56th Annual Conference on Information Sciences and Systems (CISS)

Dyslexia is a reading disability that affects children across language orthographies, despite ade... more Dyslexia is a reading disability that affects children across language orthographies, despite adequate intelligence and educational opportunity. If learning disabilities remain untreated, a child may experience long-term social and emotional problems, which may influence future success in all aspects of their lives. Early detection and intervention will help to close the gap between typically developing and reading impaired children in acquiring reading skills. We have demonstrated that animal models of dyslexia, genetic models based on candidate dyslexia susceptibility genes, and children with specific reading impairment show a common deficit on a virtual Hebb-Williams maze task. Since virtual maze task does not require oral reporting (rapid access to phonological processing) or rely on text, performance is not influenced by a potential difference in reading experience between groups. Although the correlation between dyslexia and the performance in the virtual Hebb-Williams maze task has been demonstrated, classification of atypical participants (i.e., dyslexic participants) through real-time observation of their performance on the virtual Hebb-Williams maze task is not feasible at this time. A computational model based on machine learning algorithms, that can predict reading ability based on maze learning performance, would enable real-time feedback of the performance in the form of at-risk percentages for reading. This paper presents the preliminary results of employing machine-learning based computational models to classify virtual maze performance on this task. Reading data and maze learning outcomes were analyzed from 227 school-aged children (8–14 years of age). Applying multiple variables, such as age and biological sex, into machine learning algorithms resulted in the prediction accuracy above 70%. Successful development of this predictive model would allow for early detection of risk for reading impairment, which can lead to early interventions to close the gap between typically developing and reading impaired children in acquiring reading skills.

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016

The use of an individual's neural response to stimuli (the event-related potential or ERP) ha... more The use of an individual's neural response to stimuli (the event-related potential or ERP) has potential as a biometric because it is highly resistant to fraud relative to other conventional authentication systems. P300 is an ERP in human electroencephalography (EEG) that occurs in response to an oddball stimulus when an individual is actively engaged in a target detection task. Because P300 is consistently detectable from almost every subject, it is considered a potential signal for biometric applications. This paper presents a feasibility study of using topological plots of P300 as a biometric in subject authentication. The variation in latency and location of P300 response of 24 participants performing the P300Speller task were studied. Data sets from four participants were used for algorithm training; data from the other 20 participants were used as imposters for algorithm validation. The result showed that the algorithm was able to correctly identify three out of these four participants. Validation test also proved that the algorithm was able to reject 95% of the imposters for those three authenticated participants.

2012 38th Annual Northeast Bioengineering Conference (NEBEC), 2012

ABSTRACT A brain-computer interface (BCI) relies on a classifier to determine a user's in... more ABSTRACT A brain-computer interface (BCI) relies on a classifier to determine a user's intent through the EEG signals. This classifier needs to be trained with a specific user prior to its usage. Since the effectiveness of a classifier is affected by the user's motivation during training, a memory game using the BCPy2000 platform has been developed for enhancing motivation and performance in using a traditional P300-based BCI system. A pilot study showed that this memory game is accomplishable by human subjects in a BCI system.

2013 39th Annual Northeast Bioengineering Conference, 2013

ABSTRACT Brain-computer interfaces (BCIs) are used to aid persons who have limited or no natural ... more ABSTRACT Brain-computer interfaces (BCIs) are used to aid persons who have limited or no natural motor control. The mu rhythm is often used a neural control signal because it can be voluntarily modulated using motor imagery or relaxation. Practically using mu-rhythm BCIs presents issues involving long training times, variable accuracy, and even the exclusion of some participants due to lack of control. This study used a bilateral mu-based BCI to investigate whether giving participants specific instructions on how to modulate their mu rhythm would increase mu rhythm control as compared to the usual vague instructions given.

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019

Brain-computer interface (BCI) devices are designed to bypass neuromuscular pathways enabling an ... more Brain-computer interface (BCI) devices are designed to bypass neuromuscular pathways enabling an individual to operate an external device using neural activity instead of motor activity. The use of BCIs in robotics control has important applications of assisting patients with severe motor disabilities, such as exploration, home assistance, and control of an electric wheelchair. This paper presents a development of a practical implementation of a BCI by utilizing low-cost technologies and asynchronous signal processing techniques for EEG signal acquisition and processing to navigate a quadcopter. A wireless EEG headset was used to acquire the occipital alpha and the frontal muscular artifacts from a user along with the gyroscope signals from the headset. These signals were transmitted wirelessly to a PC for further processing. Signal-processing algorithms were developed and implemented in the PC to extract the patterns from the acquired signals. These patterns, representing the intentions of the user, were then used to control a quadcopter. The design was successfully tested in a virtual environment and a physical device. The results from this preliminary pilot study proved that the design could be used with high accuracy to control a robotic device.

Genes, Brain and Behavior, 2011

Developmental reading disorder (RD) affects 5-10% of school aged children (American Psychiatric A... more Developmental reading disorder (RD) affects 5-10% of school aged children (American Psychiatric Association, 2000), with a heritability of approximately 60% (Astrom et al., 2011). Genetic association studies have identified several candidate RD susceptibility genes, including DCDC2, however a direct connection between the function of these genes and cognitive or learning impairments remains unclear (Gabel et al., 2010, Paracchini et al., 2007). Variants in DCDC2, a member of the doublecortin family of genes, have been associated in humans with RD and ADHD and Dcdc2 may play a role in neuronal migration in rats (Burbridge et al., 2008, Meng et al., 2005). In this study, we examined the effect of dcdc2 mutation on cognitive abilities in mice using a visual attention and visuo-spatial learning and memory task. We demonstrate that both heterozygous and homozygous mutations of Dcdc2 result in persistent visuo-spatial memory deficits, as well as visual discrimination and long-term memory deficits. These behavioral deficits occur in the absence of neuronal migration disruption in the mutant mice, and may be comorbid with an anxiety phenotype. These are the first results to suggest a direct relationship between induced mutation in Dcdc2 and changes in behavioral measures. Dcdc2 mutant mice should prove useful in future studies designed to further dissect the underlying neural mechanisms that are impaired following Dcdc2 mutation.

Emerging Technologies for Education, 2017

The purpose of the proposed project is to develop and validate a virtual Hebb-Williams (vHW) maze... more The purpose of the proposed project is to develop and validate a virtual Hebb-Williams (vHW) maze task for use as a low-cost, time-efficient, and easy-to-use assessment for the early detection of children at risk for reading impairment. The vHW maze offers the potential to serve as a reliable, non-language based predictor of reading difficulty, which can improve early identification and intervention efforts. Unlike current screening measures of reading impairment, the vHW maze could be administered in the classroom, with a fully integrated analytical system. With the successful attainment of this project, the vHW maze task will fill important gaps in early identification screeners by examining a broader range of cognitive processes associated with reading and enhancing our understanding of factors underlying reading impairment. This paper comprises the proposed work and significance while highlighting previous findings related to reading impairments and virtual maze environments.

Neurobiology of Disease, 2010

Reading Disability (RD) is a significant impairment in reading accuracy, speed and/or comprehensi... more Reading Disability (RD) is a significant impairment in reading accuracy, speed and/or comprehension despite adequate intelligence and educational opportunity. RD affects 5-12% of readers, has a wellestablished genetic risk, and is of unknown neurobiological cause or causes. In this review we discuss recent findings that revealed neuroanatomic anomalies in RD, studies that identified 3 candidate genes (KIAA0319, DYX1C1, and DCDC2), and compelling evidence that potentially link the function of candidate genes to the neuroanatomic anomalies. A hypothesis has emerged in which impaired neuronal migration is a cellular neurobiological antecedent to RD. We critically evaluate the evidence for this hypothesis, highlight missing evidence, and outline future research efforts that will be required to develop a more complete cellular neurobiology of RD.

Abnormal development of the cerebellum is often associated with disorders of movement, postural c... more Abnormal development of the cerebellum is often associated with disorders of movement, postural control, and motor learning. Rodent models are widely used to study normal and abnormal cerebellar development and have revealed the roles of many important genetic and environmental factors. In the present report we describe the prevalence and cytoarchitecture of molecular-layer heterotopia, a malformation of neuronal migration, in the cerebellar vermis of C57BL/6 mice and closely-related strains. In particular, we found a diverse number of cell-types affected by these malformations including Purkinje cells, granule cells, inhibitory interneurons (GABAergic and glycinergic), and glia. Heterotopia were not observed in a sample of wild-derived mice, outbred mice, or inbred mice not closely related to C57BL/6 mice. These data are relevant to the use of C57BL/6 mice as models in the study of brain and behavior relationships and provide greater understanding of human cerebellar dysplasia.

Developmental Neuroscience, 2014

Human neocortical molecular layer heterotopia consist of aggregations of hundreds of neurons and ... more Human neocortical molecular layer heterotopia consist of aggregations of hundreds of neurons and glia in the molecular layer (layer I) and are indicative of neuronal migration defect. Despite having been associated with dyslexia, epilepsy, cobblestone lissencephaly, polymicrogyria, and Fukuyama muscular dystrophy, a complete understanding of the cellular and axonal constituents of molecular layer heterotopia is lacking. Using a mouse model, we identify diverse excitatory and inhibitory neurons as well as glia in heterotopia based on molecular profiles. Using immunocytochemistry, we identify diverse afferents in heterotopia from subcortical neuromodulatory centers. Finally, we document intracortical projections to/from heterotopia. These data are relevant toward understanding how heterotopia affect brain function in diverse neurodevelopmental disorders.

Annals of Dyslexia, 2016

Direct relationships between induced mutation in the DCDC2 candidate dyslexia susceptibility gene... more Direct relationships between induced mutation in the DCDC2 candidate dyslexia susceptibility gene in mice and changes in behavioral measures of visual spatial learning have been reported. We were interested in determining whether performance on a visual-spatial learning and memory task could be translated across species (study 1) and whether children with reading impairment showed a similar impairment to animal models of the disorder (study 2). Study 1 included 37 participants who completed six trials of four different virtual Hebb-Williams maze configurations. A 2 × 4 × 6 mixed factorial repeated measures ANOVA indicated consistency in performance between humans and mice on these tasks, enabling us to translate across species. Study 2 included a total of 91 participants (age range = 8-13 years). Eighteen participants were identified with reading disorder by performance on the Woodcock-Johnson III Tests of Achievement. Participants completed six trials of five separate virtual Hebb-Williams maze configurations. A 2 × 5 × 6 mixed factorial ANCOVA (gender as covariate) indicated that individuals with reading impairment demonstrated impaired visuo-spatial performance on this task. Overall, results from this study suggest that we are able to translate behavioral deficits observed in genetic animal models of dyslexia to humans with reading impairment. Future studies will utilize the virtual environment to further explore the underlying basis for this impairment.

2015 41st Annual Northeast Biomedical Engineering Conference (NEBEC), 2015

2012 38th Annual Northeast Bioengineering Conference (NEBEC), 2012

An event classifier has been developed to analyze the collected EEG signals and distinguish betwe... more An event classifier has been developed to analyze the collected EEG signals and distinguish between different events. The classifier introduced in this study was based on an artificial neural network model. The training process of the neural network required large amount of sample data and target results. A trained artificial neural network can then predict the outcome of an event based on the information of the corresponding EEG signal. The architecture of the artificial neural network involved hidden layers in addition to the input and output layers, which satisfied the non-linearity of the problem that the classifier was designed to solve. Experiments were conducted to validate this approach by using the classifier to distinguish whether subjects placed their fingers into hot or cold water. Validation results demonstrated the effectiveness of the classifier and its potential application in other fields.

2015 41st Annual Northeast Biomedical Engineering Conference (NEBEC), 2015

ABSTRACT A brain-computer interface (BCI) relies on a classifier to determine a user's in... more ABSTRACT A brain-computer interface (BCI) relies on a classifier to determine a user's intent through the EEG signals. This classifier needs to be trained with a specific user prior to its usage. Since the effectiveness of a classifier is affected by the user's motivation during training, a memory game using the BCPy2000 platform has been developed for enhancing motivation and performance in using a traditional P300-based BCI system. A pilot study showed that this memory game is accomplishable by human subjects in a BCI system.

Developmental Neuroscience

Reading disability (RD), which affects between 5 and 17% of the population worldwide, is the most... more Reading disability (RD), which affects between 5 and 17% of the population worldwide, is the most prevalent form of learning disability, and is associated with underactivation of a universal reading network in children. However, recent research suggests there are differences in learning rates on cognitive predictors of reading performance, as well as differences in activation patterns within the reading neural network, based on orthographic depth (i.e., transparent/shallow vs. deep/opaque orthographies) in children with RD. Recently, we showed that native English-speaking children with RD exhibit impaired performance on a maze learning task that taps into the same neural networks that are activated during reading. In addition, we demonstrated that genetic risk for RD strengthens the relationship between reading impairment and maze learning performance. However, it is unclear whether the results from these studies can be broadly applied to children from other language orthographies. ...

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Brain-computer interface (BCI) devices are designed to bypass neuromuscular pathways enabling an ... more Brain-computer interface (BCI) devices are designed to bypass neuromuscular pathways enabling an individual to operate an external device using neural activity, as opposed to motor activity. Neural outputs are detected and utilized to drive a command signal to control a device, such as a wheelchair. BCIs provide individuals with severe motor impairment with an alternative mechanism to interact with their environment. Ultimately BCIs help to restore some degree of autonomy to an individual with limited motor control. Increased autonomy will likely lead to an enhanced quality of life of the individual as well as the caregiver(s). In this paper, we developed a BCI-controlled, ground-based robot. A wireless EEG headset was used to obtain the EEG signals from the user and stream the signals wirelessly into a host computer. The computer processed the EEG signals, extracted possible features from the signals to interpret the user's intents as the control commands, and then sent the command signals wirelessly to navigate the robot. This paper discusses the methodology used in the design as well as the response speed and accuracy in robot navigation from the test results. Future applications in assistive technology by using similar design concept are also discussed.

Developmental Neuroscience

Dyslexia is a common learning disability that affects processing of written language despite adeq... more Dyslexia is a common learning disability that affects processing of written language despite adequate intelligence and educational background. If learning disabilities remain untreated, a child may experience long-term social and emotional problems, which influence future success in all aspects of their life. Dyslexia has a 60% heritability rate, and genetic studies have identified multiple dyslexia susceptibility genes (DSGs). DSGs, such as DCDC2, are consistently associated with the risk and severity of reading disability (RD). Altered neural connectivity within temporoparietal regions of the brain is associated with specific variants of DSGs in individuals with RD. Genetically altering DSG expression in mice results in visual and auditory processing deficits as well as neurophysiological and neuroanatomical disruptions. Previously, we demonstrated that learning deficits associated with RD can be translated across species using virtual environments. In this 2-year longitudinal stu...

2022 56th Annual Conference on Information Sciences and Systems (CISS)

Dyslexia is a reading disability that affects children across language orthographies, despite ade... more Dyslexia is a reading disability that affects children across language orthographies, despite adequate intelligence and educational opportunity. If learning disabilities remain untreated, a child may experience long-term social and emotional problems, which may influence future success in all aspects of their lives. Early detection and intervention will help to close the gap between typically developing and reading impaired children in acquiring reading skills. We have demonstrated that animal models of dyslexia, genetic models based on candidate dyslexia susceptibility genes, and children with specific reading impairment show a common deficit on a virtual Hebb-Williams maze task. Since virtual maze task does not require oral reporting (rapid access to phonological processing) or rely on text, performance is not influenced by a potential difference in reading experience between groups. Although the correlation between dyslexia and the performance in the virtual Hebb-Williams maze task has been demonstrated, classification of atypical participants (i.e., dyslexic participants) through real-time observation of their performance on the virtual Hebb-Williams maze task is not feasible at this time. A computational model based on machine learning algorithms, that can predict reading ability based on maze learning performance, would enable real-time feedback of the performance in the form of at-risk percentages for reading. This paper presents the preliminary results of employing machine-learning based computational models to classify virtual maze performance on this task. Reading data and maze learning outcomes were analyzed from 227 school-aged children (8–14 years of age). Applying multiple variables, such as age and biological sex, into machine learning algorithms resulted in the prediction accuracy above 70%. Successful development of this predictive model would allow for early detection of risk for reading impairment, which can lead to early interventions to close the gap between typically developing and reading impaired children in acquiring reading skills.

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016

The use of an individual's neural response to stimuli (the event-related potential or ERP) ha... more The use of an individual's neural response to stimuli (the event-related potential or ERP) has potential as a biometric because it is highly resistant to fraud relative to other conventional authentication systems. P300 is an ERP in human electroencephalography (EEG) that occurs in response to an oddball stimulus when an individual is actively engaged in a target detection task. Because P300 is consistently detectable from almost every subject, it is considered a potential signal for biometric applications. This paper presents a feasibility study of using topological plots of P300 as a biometric in subject authentication. The variation in latency and location of P300 response of 24 participants performing the P300Speller task were studied. Data sets from four participants were used for algorithm training; data from the other 20 participants were used as imposters for algorithm validation. The result showed that the algorithm was able to correctly identify three out of these four participants. Validation test also proved that the algorithm was able to reject 95% of the imposters for those three authenticated participants.

2012 38th Annual Northeast Bioengineering Conference (NEBEC), 2012

ABSTRACT A brain-computer interface (BCI) relies on a classifier to determine a user's in... more ABSTRACT A brain-computer interface (BCI) relies on a classifier to determine a user's intent through the EEG signals. This classifier needs to be trained with a specific user prior to its usage. Since the effectiveness of a classifier is affected by the user's motivation during training, a memory game using the BCPy2000 platform has been developed for enhancing motivation and performance in using a traditional P300-based BCI system. A pilot study showed that this memory game is accomplishable by human subjects in a BCI system.

2013 39th Annual Northeast Bioengineering Conference, 2013

ABSTRACT Brain-computer interfaces (BCIs) are used to aid persons who have limited or no natural ... more ABSTRACT Brain-computer interfaces (BCIs) are used to aid persons who have limited or no natural motor control. The mu rhythm is often used a neural control signal because it can be voluntarily modulated using motor imagery or relaxation. Practically using mu-rhythm BCIs presents issues involving long training times, variable accuracy, and even the exclusion of some participants due to lack of control. This study used a bilateral mu-based BCI to investigate whether giving participants specific instructions on how to modulate their mu rhythm would increase mu rhythm control as compared to the usual vague instructions given.

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019

Brain-computer interface (BCI) devices are designed to bypass neuromuscular pathways enabling an ... more Brain-computer interface (BCI) devices are designed to bypass neuromuscular pathways enabling an individual to operate an external device using neural activity instead of motor activity. The use of BCIs in robotics control has important applications of assisting patients with severe motor disabilities, such as exploration, home assistance, and control of an electric wheelchair. This paper presents a development of a practical implementation of a BCI by utilizing low-cost technologies and asynchronous signal processing techniques for EEG signal acquisition and processing to navigate a quadcopter. A wireless EEG headset was used to acquire the occipital alpha and the frontal muscular artifacts from a user along with the gyroscope signals from the headset. These signals were transmitted wirelessly to a PC for further processing. Signal-processing algorithms were developed and implemented in the PC to extract the patterns from the acquired signals. These patterns, representing the intentions of the user, were then used to control a quadcopter. The design was successfully tested in a virtual environment and a physical device. The results from this preliminary pilot study proved that the design could be used with high accuracy to control a robotic device.

Genes, Brain and Behavior, 2011

Developmental reading disorder (RD) affects 5-10% of school aged children (American Psychiatric A... more Developmental reading disorder (RD) affects 5-10% of school aged children (American Psychiatric Association, 2000), with a heritability of approximately 60% (Astrom et al., 2011). Genetic association studies have identified several candidate RD susceptibility genes, including DCDC2, however a direct connection between the function of these genes and cognitive or learning impairments remains unclear (Gabel et al., 2010, Paracchini et al., 2007). Variants in DCDC2, a member of the doublecortin family of genes, have been associated in humans with RD and ADHD and Dcdc2 may play a role in neuronal migration in rats (Burbridge et al., 2008, Meng et al., 2005). In this study, we examined the effect of dcdc2 mutation on cognitive abilities in mice using a visual attention and visuo-spatial learning and memory task. We demonstrate that both heterozygous and homozygous mutations of Dcdc2 result in persistent visuo-spatial memory deficits, as well as visual discrimination and long-term memory deficits. These behavioral deficits occur in the absence of neuronal migration disruption in the mutant mice, and may be comorbid with an anxiety phenotype. These are the first results to suggest a direct relationship between induced mutation in Dcdc2 and changes in behavioral measures. Dcdc2 mutant mice should prove useful in future studies designed to further dissect the underlying neural mechanisms that are impaired following Dcdc2 mutation.

Emerging Technologies for Education, 2017

The purpose of the proposed project is to develop and validate a virtual Hebb-Williams (vHW) maze... more The purpose of the proposed project is to develop and validate a virtual Hebb-Williams (vHW) maze task for use as a low-cost, time-efficient, and easy-to-use assessment for the early detection of children at risk for reading impairment. The vHW maze offers the potential to serve as a reliable, non-language based predictor of reading difficulty, which can improve early identification and intervention efforts. Unlike current screening measures of reading impairment, the vHW maze could be administered in the classroom, with a fully integrated analytical system. With the successful attainment of this project, the vHW maze task will fill important gaps in early identification screeners by examining a broader range of cognitive processes associated with reading and enhancing our understanding of factors underlying reading impairment. This paper comprises the proposed work and significance while highlighting previous findings related to reading impairments and virtual maze environments.

Neurobiology of Disease, 2010

Reading Disability (RD) is a significant impairment in reading accuracy, speed and/or comprehensi... more Reading Disability (RD) is a significant impairment in reading accuracy, speed and/or comprehension despite adequate intelligence and educational opportunity. RD affects 5-12% of readers, has a wellestablished genetic risk, and is of unknown neurobiological cause or causes. In this review we discuss recent findings that revealed neuroanatomic anomalies in RD, studies that identified 3 candidate genes (KIAA0319, DYX1C1, and DCDC2), and compelling evidence that potentially link the function of candidate genes to the neuroanatomic anomalies. A hypothesis has emerged in which impaired neuronal migration is a cellular neurobiological antecedent to RD. We critically evaluate the evidence for this hypothesis, highlight missing evidence, and outline future research efforts that will be required to develop a more complete cellular neurobiology of RD.

Abnormal development of the cerebellum is often associated with disorders of movement, postural c... more Abnormal development of the cerebellum is often associated with disorders of movement, postural control, and motor learning. Rodent models are widely used to study normal and abnormal cerebellar development and have revealed the roles of many important genetic and environmental factors. In the present report we describe the prevalence and cytoarchitecture of molecular-layer heterotopia, a malformation of neuronal migration, in the cerebellar vermis of C57BL/6 mice and closely-related strains. In particular, we found a diverse number of cell-types affected by these malformations including Purkinje cells, granule cells, inhibitory interneurons (GABAergic and glycinergic), and glia. Heterotopia were not observed in a sample of wild-derived mice, outbred mice, or inbred mice not closely related to C57BL/6 mice. These data are relevant to the use of C57BL/6 mice as models in the study of brain and behavior relationships and provide greater understanding of human cerebellar dysplasia.

Developmental Neuroscience, 2014

Human neocortical molecular layer heterotopia consist of aggregations of hundreds of neurons and ... more Human neocortical molecular layer heterotopia consist of aggregations of hundreds of neurons and glia in the molecular layer (layer I) and are indicative of neuronal migration defect. Despite having been associated with dyslexia, epilepsy, cobblestone lissencephaly, polymicrogyria, and Fukuyama muscular dystrophy, a complete understanding of the cellular and axonal constituents of molecular layer heterotopia is lacking. Using a mouse model, we identify diverse excitatory and inhibitory neurons as well as glia in heterotopia based on molecular profiles. Using immunocytochemistry, we identify diverse afferents in heterotopia from subcortical neuromodulatory centers. Finally, we document intracortical projections to/from heterotopia. These data are relevant toward understanding how heterotopia affect brain function in diverse neurodevelopmental disorders.

Annals of Dyslexia, 2016

Direct relationships between induced mutation in the DCDC2 candidate dyslexia susceptibility gene... more Direct relationships between induced mutation in the DCDC2 candidate dyslexia susceptibility gene in mice and changes in behavioral measures of visual spatial learning have been reported. We were interested in determining whether performance on a visual-spatial learning and memory task could be translated across species (study 1) and whether children with reading impairment showed a similar impairment to animal models of the disorder (study 2). Study 1 included 37 participants who completed six trials of four different virtual Hebb-Williams maze configurations. A 2 × 4 × 6 mixed factorial repeated measures ANOVA indicated consistency in performance between humans and mice on these tasks, enabling us to translate across species. Study 2 included a total of 91 participants (age range = 8-13 years). Eighteen participants were identified with reading disorder by performance on the Woodcock-Johnson III Tests of Achievement. Participants completed six trials of five separate virtual Hebb-Williams maze configurations. A 2 × 5 × 6 mixed factorial ANCOVA (gender as covariate) indicated that individuals with reading impairment demonstrated impaired visuo-spatial performance on this task. Overall, results from this study suggest that we are able to translate behavioral deficits observed in genetic animal models of dyslexia to humans with reading impairment. Future studies will utilize the virtual environment to further explore the underlying basis for this impairment.

2015 41st Annual Northeast Biomedical Engineering Conference (NEBEC), 2015

2012 38th Annual Northeast Bioengineering Conference (NEBEC), 2012

An event classifier has been developed to analyze the collected EEG signals and distinguish betwe... more An event classifier has been developed to analyze the collected EEG signals and distinguish between different events. The classifier introduced in this study was based on an artificial neural network model. The training process of the neural network required large amount of sample data and target results. A trained artificial neural network can then predict the outcome of an event based on the information of the corresponding EEG signal. The architecture of the artificial neural network involved hidden layers in addition to the input and output layers, which satisfied the non-linearity of the problem that the classifier was designed to solve. Experiments were conducted to validate this approach by using the classifier to distinguish whether subjects placed their fingers into hot or cold water. Validation results demonstrated the effectiveness of the classifier and its potential application in other fields.

2015 41st Annual Northeast Biomedical Engineering Conference (NEBEC), 2015