Contact
Biomedical Research Center251 Bayview Boulevard
Baltimore, MD 21224
Phone: 667-312-5261
Email: tross@mail.nih.gov
Education
B.S. - Electrical Engineering and Applied Physics, Illinois Institute of Technology
M.S. and Ph.D. - Physics, University of California, Irvine
Research Interests
Tom received his B.S. in Electrical Engineering and Applied Physics from the Illinois Institute of Technology (IIT, like MIT only in the Midwest, without the reputation, facility or quality of students – wait, it is nothing like MIT). He went on to get his M.S. and Ph.D. from the University of California, Irvine (Go Anteaters!), in Physics – specifically experimental plasma physics (plasma = ionized gas like the sun, not plasma = juicy parts of blood). How does plasma physics relate to fMRI? Good question! Answer: It doesn’t. So, how did he get into fMRI? Another good question (damn you are smart)! It was through dumb luck and excellent timing that he had contacted Elliot Stein at a time when he needed a job and Elliot needed someone to do a job. This was in 1997 – early days for fMRI and a time when pretty much everybody was coming into the field from somewhere else. So, his signal processing/data analysis skills translated pretty nicely, and the rest he learned/made up along the way. In 2002, Dr. Stein moved to NIDA and Tom joined him there as a Staff Scientist.
Dr. Ross’s role as a Staff Scientist in the branch has him involved in most of the clinical projects. His own research interests center around advanced fMRI analysis techniques. Specifically, he is working on using multimodal MRI data for predictive modeling of addiction.
Publications
Selected Publications
2022
Angebrandt, Alexanndra; Abulseoud, Osama A.; Kisner, Mallory; Diazgranados, Nancy; Momenan, Reza; Yang, Yihong; Stein, Elliot A.; Ross, Thomas J.
Dose-dependent Relationship between Social Drinking and Brain Aging Journal Article
In: Neurobiology of Aging, vol. 111, pp. 71–81, 2022, ISSN: 0197-4580.
@article{ross_T_DoseDependentRelationship2022,
title = {Dose-dependent Relationship between Social Drinking and Brain Aging},
author = {Alexanndra Angebrandt and Osama A. Abulseoud and Mallory Kisner and Nancy Diazgranados and Reza Momenan and Yihong Yang and Elliot A. Stein and Thomas J. Ross},
url = {https://pubmed.ncbi.nlm.nih.gov/34973470/},
doi = {10.1016/j.neurobiolaging.2021.11.008},
issn = {0197-4580},
year = {2022},
date = {2022-03-01},
urldate = {2022-03-01},
journal = {Neurobiology of Aging},
volume = {111},
pages = {71--81},
abstract = {Low-level alcohol consumption is commonly perceived as being inconsequential or even beneficial for overall health, with some reports suggesting that it may protect against dementia or cardiovascular risks. However, these potential benefits do not preclude the concurrent possibility of negative health outcomes related to alcohol consumption. To examine whether casual, non-heavy drinking is associated with premature brain aging, we utilized the Brain-Age Regression Analysis and Computational Utility Software package to predict brain age in a community sample of adults [n~=~240, mean age 35.1 ($pm$10.7) years, 48% male, 49% African American]. Accelerated brain aging was operationalized as the difference between predicted and chronological age (``brain age gap''). Multiple regression analysis revealed a significant association between previous 90-day alcohol consumption and brain age gap ($beta~$=~0.014, p~=~0.023). We replicated these results in an independent cohort [n~=~231 adults, mean age 34.3 ($pm$11.1) years, 55% male, 28% African American: $beta~$=~0.014, p~=~0.002]. Our results suggest that even low-level alcohol consumption is associated with premature brain aging. The clinical significance of these findings remains to be investigated.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Low-level alcohol consumption is commonly perceived as being inconsequential or even beneficial for overall health, with some reports suggesting that it may protect against dementia or cardiovascular risks. However, these potential benefits do not preclude the concurrent possibility of negative health outcomes related to alcohol consumption. To examine whether casual, non-heavy drinking is associated with premature brain aging, we utilized the Brain-Age Regression Analysis and Computational Utility Software package to predict brain age in a community sample of adults [n~=~240, mean age 35.1 ($pm$10.7) years, 48% male, 49% African American]. Accelerated brain aging was operationalized as the difference between predicted and chronological age (``brain age gap''). Multiple regression analysis revealed a significant association between previous 90-day alcohol consumption and brain age gap ($beta~$=~0.014, p~=~0.023). We replicated these results in an independent cohort [n~=~231 adults, mean age 34.3 ($pm$11.1) years, 55% male, 28% African American: $beta~$=~0.014, p~=~0.002]. Our results suggest that even low-level alcohol consumption is associated with premature brain aging. The clinical significance of these findings remains to be investigated.
Deshpande, Harshawardhan U.; Fedota, John R.; Castillo, Juan; Salmeron, Betty Jo; Ross, Thomas J.; Stein, Elliot A.
Not All Smokers Are Alike: The Hidden Cost of Sustained Attention during Nicotine Abstinence Journal Article
In: Neuropsychopharmacology, pp. 1–10, 2022, ISSN: 1740-634X.
@article{stein_E_NotAllSmokers2022,
title = {Not All Smokers Are Alike: The Hidden Cost of Sustained Attention during Nicotine Abstinence},
author = {Harshawardhan U. Deshpande and John R. Fedota and Juan Castillo and Betty Jo Salmeron and Thomas J. Ross and Elliot A. Stein},
url = {https://pubmed.ncbi.nlm.nih.gov/35091674/},
doi = {10.1038/s41386-022-01275-8},
issn = {1740-634X},
year = {2022},
date = {2022-01-28},
urldate = {2022-01-28},
journal = {Neuropsychopharmacology},
pages = {1--10},
publisher = {Nature Publishing Group},
abstract = {Nicotine Withdrawal Syndrome (NWS)-associated cognitive deficits are notably heterogeneous, suggesting underlying endophenotypic variance. However, parsing this variance in smokers has remained challenging. In this study, we identified smoker subgroups based on response accuracy during a Parametric Flanker Task (PFT) and then characterized distinct neuroimaging endophenotypes using a nicotine state manipulation. Smokers completed the PFT in two fMRI sessions (nicotine sated, abstinent). Based on response accuracy in the stressful, high cognitive demand PFT condition, smokers split into high (HTP},
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Nicotine Withdrawal Syndrome (NWS)-associated cognitive deficits are notably heterogeneous, suggesting underlying endophenotypic variance. However, parsing this variance in smokers has remained challenging. In this study, we identified smoker subgroups based on response accuracy during a Parametric Flanker Task (PFT) and then characterized distinct neuroimaging endophenotypes using a nicotine state manipulation. Smokers completed the PFT in two fMRI sessions (nicotine sated, abstinent). Based on response accuracy in the stressful, high cognitive demand PFT condition, smokers split into high (HTP
2021
Korponay, Cole; Stein, Elliot A; Ross, Thomas J
Laterality Hotspots in the Striatum Journal Article
In: Cereb Cortex, 2021, ISSN: 1460-2199.
@article{pmid34727171,
title = {Laterality Hotspots in the Striatum},
author = {Cole Korponay and Elliot A Stein and Thomas J Ross},
url = {https://pubmed.ncbi.nlm.nih.gov/34727171/},
doi = {10.1093/cercor/bhab392},
issn = {1460-2199},
year = {2021},
date = {2021-11-01},
urldate = {2021-11-01},
journal = {Cereb Cortex},
abstract = {Striatal loci are connected to both the ipsilateral and contralateral frontal cortex. Normative quantitation of the dissimilarity between striatal loci's hemispheric connection profiles and its spatial variance across the striatum, and assessment of how interindividual differences relate to function, stands to further the understanding of the role of corticostriatal circuits in lateralized functions and the role of abnormal corticostriatal laterality in neurodevelopmental and other neuropsychiatric disorders. A resting-state functional connectivity fingerprinting approach (n = 261) identified "laterality hotspots"-loci whose profiles of connectivity with ipsilateral and contralateral frontal cortex were disproportionately dissimilar-in the right rostral ventral putamen, left rostral central caudate, and bilateral caudal ventral caudate. Findings were replicated in an independent sample and were robust to both preprocessing choices and the choice of cortical atlas used for parcellation definitions. Across subjects, greater rightward connectional laterality at the right ventral putamen hotspot and greater leftward connectional laterality at the left rostral caudate hotspot were associated with higher performance on tasks engaging lateralized functions (i.e., response inhibition and language, respectively). In sum, we find robust and reproducible evidence for striatal loci with disproportionately lateralized connectivity profiles where interindividual differences in laterality magnitude are associated with behavioral capacities on lateralized functions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Striatal loci are connected to both the ipsilateral and contralateral frontal cortex. Normative quantitation of the dissimilarity between striatal loci's hemispheric connection profiles and its spatial variance across the striatum, and assessment of how interindividual differences relate to function, stands to further the understanding of the role of corticostriatal circuits in lateralized functions and the role of abnormal corticostriatal laterality in neurodevelopmental and other neuropsychiatric disorders. A resting-state functional connectivity fingerprinting approach (n = 261) identified "laterality hotspots"-loci whose profiles of connectivity with ipsilateral and contralateral frontal cortex were disproportionately dissimilar-in the right rostral ventral putamen, left rostral central caudate, and bilateral caudal ventral caudate. Findings were replicated in an independent sample and were robust to both preprocessing choices and the choice of cortical atlas used for parcellation definitions. Across subjects, greater rightward connectional laterality at the right ventral putamen hotspot and greater leftward connectional laterality at the left rostral caudate hotspot were associated with higher performance on tasks engaging lateralized functions (i.e., response inhibition and language, respectively). In sum, we find robust and reproducible evidence for striatal loci with disproportionately lateralized connectivity profiles where interindividual differences in laterality magnitude are associated with behavioral capacities on lateralized functions.
2020
Lesage, E.; Sutherland, M. T.; Ross, T. J.; Salmeron, B. J.; Stein, E. A.
In: Neuropsychopharmacology, vol. 45, no. 5, pp. 857–865, 2020, ISSN: 1740-634X.
@article{lesageNicotineDependenceTrait2020,
title = {Nicotine Dependence (Trait) and Acute Nicotinic Stimulation (State) Modulate Attention but Not Inhibitory Control: Converging fMRI Evidence from Go–Nogo and Flanker Tasks},
author = {E. Lesage and M. T. Sutherland and T. J. Ross and B. J. Salmeron and E. A. Stein},
url = {https://pubmed.ncbi.nlm.nih.gov/31995811/},
doi = {10.1038/s41386-020-0623-1},
issn = {1740-634X},
year = {2020},
date = {2020-04-01},
urldate = {2020-04-01},
journal = {Neuropsychopharmacology},
volume = {45},
number = {5},
pages = {857--865},
publisher = {Nature Publishing Group},
abstract = {Cognitive deficits during nicotine withdrawal may contribute to smoking relapse. However, interacting effects of chronic nicotine dependence and acute nicotine withdrawal on cognitive control are poorly understood. Here we examine the effects of nicotine dependence (trait; smokers (n,=,24) vs. non-smoking controls; n,=,20) and acute nicotinic stimulation (state; administration of nicotine and varenicline, two FDA-approved smoking cessation aids, during abstinence), on two well-established tests of inhibitory control, the Go– Nogo task and the Flanker task, during fMRI scanning. We compared performance and neural responses between these four pharmacological manipulations in a double-blind, placebo-controlled crossover design. As expected, performance in both tasks was modulated by nicotine dependence, abstinence, and pharmacological manipulation. However, effects were driven entirely by conditions that required less inhibitory control. When demand for inhibitory control was high, abstinent smokers showed no deficits. By contrast, acutely abstinent smokers showed performance deficits in easier conditions and missed more trials. Go– Nogo fMRI results showed decreased inhibition-related neural activity in right anterior insula and right putamen in smokers and decreased dorsal anterior cingulate cortex activity on nicotine across groups. No effects were found on inhibition-related activity during the Flanker task or on error-related activity in either task. Given robust nicotinic effects on physiology and behavioral deficits in attention, we are confident that pharmacological manipulations were effective. Thus findings fit a recent proposal that abstinent smokers show decreased ability to divert cognitive resources at low or intermediate cognitive demand, while performance at high cognitive demand remains relatively unaffected, suggesting a primary attentional deficit during acute abstinence.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Cognitive deficits during nicotine withdrawal may contribute to smoking relapse. However, interacting effects of chronic nicotine dependence and acute nicotine withdrawal on cognitive control are poorly understood. Here we examine the effects of nicotine dependence (trait; smokers (n,=,24) vs. non-smoking controls; n,=,20) and acute nicotinic stimulation (state; administration of nicotine and varenicline, two FDA-approved smoking cessation aids, during abstinence), on two well-established tests of inhibitory control, the Go– Nogo task and the Flanker task, during fMRI scanning. We compared performance and neural responses between these four pharmacological manipulations in a double-blind, placebo-controlled crossover design. As expected, performance in both tasks was modulated by nicotine dependence, abstinence, and pharmacological manipulation. However, effects were driven entirely by conditions that required less inhibitory control. When demand for inhibitory control was high, abstinent smokers showed no deficits. By contrast, acutely abstinent smokers showed performance deficits in easier conditions and missed more trials. Go– Nogo fMRI results showed decreased inhibition-related neural activity in right anterior insula and right putamen in smokers and decreased dorsal anterior cingulate cortex activity on nicotine across groups. No effects were found on inhibition-related activity during the Flanker task or on error-related activity in either task. Given robust nicotinic effects on physiology and behavioral deficits in attention, we are confident that pharmacological manipulations were effective. Thus findings fit a recent proposal that abstinent smokers show decreased ability to divert cognitive resources at low or intermediate cognitive demand, while performance at high cognitive demand remains relatively unaffected, suggesting a primary attentional deficit during acute abstinence.
Abulseoud, Osama A; Ross, Thomas J; Nam, Hyung Wook; Caparelli, Elisabeth C; Tennekoon, Michael; Schleyer, Brooke; Castillo, Juan; Fedota, John; Gu, Hong; Yang, Yihong; Stein, Elliot
In: Neuropsychopharmacology, vol. 45, no. 11, pp. 1920–1930, 2020, ISBN: 1740-634X.
@article{Abulseoud:2020aa,
title = {Short-term nicotine deprivation alters dorsal anterior cingulate glutamate concentration and concomitant cingulate-cortical functional connectivity},
author = {Osama A Abulseoud and Thomas J Ross and Hyung Wook Nam and Elisabeth C Caparelli and Michael Tennekoon and Brooke Schleyer and Juan Castillo and John Fedota and Hong Gu and Yihong Yang and Elliot Stein},
url = {https://pubmed.ncbi.nlm.nih.gov/32559759/},
doi = {10.1038/s41386-020-0741-9},
isbn = {1740-634X},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Neuropsychopharmacology},
volume = {45},
number = {11},
pages = {1920--1930},
abstract = {Most cigarette smokers who wish to quit too often relapse within the first few days of abstinence, primarily due to the aversive aspects of the nicotine withdrawal syndrome (NWS), which remains poorly understood. Considerable research has suggested that the dorsal anterior cingulate cortex (dACC) plays a key role in nicotine dependence, with its functional connections between other brain regions altered as a function of trait addiction and state withdrawal. The flow of information between dACC and fronto-striatal regions is secured through different pathways, the vast majority of which are glutamatergic. As such, we investigated dACC activity using resting state functional connectivity (rsFC) with functional magnetic resonance imaging (fMRI) and glutamate (Glu) concentration with magnetic resonance spectroscopy (MRS). We also investigated the changes in adenosine levels in plasma during withdrawal as a surrogate for brain adenosine, which plays a role in fine-tuning synaptic glutamate transmission. Using a double-blind, placebo-controlled, randomized crossover design, nontreatment seeking smoking participants (N = 30) completed two imaging sessions, one while nicotine sated and another after 36 h nicotine abstinence. We observed reduced dACC Glu (P = 0.029) along with a significant reduction in plasma adenosine (P = 0.03) and adenosine monophosphate (AMP; P < 0.0001) concentrations during nicotine withdrawal in comparison with nicotine sated state. This withdrawal state manipulation also led to an increase in rsFC strength (P < 0.05) between dACC and several frontal cortical regions, including left superior frontal gyrus (LSFG), and right middle frontal gyrus (RMFG). Moreover, the state-trait changes in dACC Glu and rsFC strength between the dACC and both SFG and MFG were positively correlated (P = 0.012, and P = 0.007, respectively). Finally, the change in circuit strength between dACC and LSFG was negatively correlated with the change in withdrawal symptom manifestations as measured by the Wisconsin Smoking Withdrawal Scale (P = 0.04) and Tobacco Craving Questionnaire (P = 0.014). These multimodal imaging-behavioral findings reveal the complex cascade of changes induced by acute nicotine deprivation and call for further investigation into the potential utility of adenosine- and glutamate-signaling as novel therapeutic targets to treat the NWS.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Most cigarette smokers who wish to quit too often relapse within the first few days of abstinence, primarily due to the aversive aspects of the nicotine withdrawal syndrome (NWS), which remains poorly understood. Considerable research has suggested that the dorsal anterior cingulate cortex (dACC) plays a key role in nicotine dependence, with its functional connections between other brain regions altered as a function of trait addiction and state withdrawal. The flow of information between dACC and fronto-striatal regions is secured through different pathways, the vast majority of which are glutamatergic. As such, we investigated dACC activity using resting state functional connectivity (rsFC) with functional magnetic resonance imaging (fMRI) and glutamate (Glu) concentration with magnetic resonance spectroscopy (MRS). We also investigated the changes in adenosine levels in plasma during withdrawal as a surrogate for brain adenosine, which plays a role in fine-tuning synaptic glutamate transmission. Using a double-blind, placebo-controlled, randomized crossover design, nontreatment seeking smoking participants (N = 30) completed two imaging sessions, one while nicotine sated and another after 36 h nicotine abstinence. We observed reduced dACC Glu (P = 0.029) along with a significant reduction in plasma adenosine (P = 0.03) and adenosine monophosphate (AMP; P < 0.0001) concentrations during nicotine withdrawal in comparison with nicotine sated state. This withdrawal state manipulation also led to an increase in rsFC strength (P < 0.05) between dACC and several frontal cortical regions, including left superior frontal gyrus (LSFG), and right middle frontal gyrus (RMFG). Moreover, the state-trait changes in dACC Glu and rsFC strength between the dACC and both SFG and MFG were positively correlated (P = 0.012, and P = 0.007, respectively). Finally, the change in circuit strength between dACC and LSFG was negatively correlated with the change in withdrawal symptom manifestations as measured by the Wisconsin Smoking Withdrawal Scale (P = 0.04) and Tobacco Craving Questionnaire (P = 0.014). These multimodal imaging-behavioral findings reveal the complex cascade of changes induced by acute nicotine deprivation and call for further investigation into the potential utility of adenosine- and glutamate-signaling as novel therapeutic targets to treat the NWS.
Fedota, John R; Ross, Thomas J; Castillo, Juan; McKenna, Michael R; Matous, Allison L; Salmeron, Betty Jo; Menon, Vinod; Stein, Elliot A
Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence Journal Article
In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2020, ISSN: 2451-9022.
@article{FEDOTA2020b,
title = {Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence},
author = {John R Fedota and Thomas J Ross and Juan Castillo and Michael R McKenna and Allison L Matous and Betty Jo Salmeron and Vinod Menon and Elliot A Stein},
url = {https://pubmed.ncbi.nlm.nih.gov/33436331/},
doi = {https://doi.org/10.1016/j.bpsc.2020.10.004},
issn = {2451-9022},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
abstract = {\textbf{Background}
The nicotine withdrawal syndrome (NWS) includes affective and cognitive disruptions whose incidence and severity vary across time during acute abstinence. However, most network-level neuroimaging uses static measures of resting-state functional connectivity and assumes time-invariance and is thus unable to capture dynamic brain-behavior relationships. Recent advances in resting-state functional connectivity signal processing allow characterization of time-varying functional connectivity (TVFC), which characterizes network communication between networks that reconfigure over the course of data collection. Therefore, TVFC may more fully describe network dysfunction related to the NWS.
\textbf{Methods}
To isolate alterations in the frequency and diversity of communication across network boundaries during acute nicotine abstinence, we scanned 25 cigarette smokers in the nicotine-sated and abstinent states and applied a previously validated method to characterize TVFC at a network and a nodal level within the brain.
\textbf{Results}
During abstinence, we found brain-wide decreases in the frequency of interactions between network nodes in different modular communities (i.e., temporal flexibility). In addition, within a subset of the networks examined, the variability of these interactions across community boundaries (i.e., spatiotemporal diversity) also decreased. Finally, within 2 of these networks, the decrease in spatiotemporal diversity was significantly related to NWS clinical symptoms.
\textbf{Conclusions}
Using multiple measures of TVFC in a within-subjects design, we characterized a novel set of changes in network communication and linked these changes to specific behavioral symptoms of the NWS. These reductions in TVFC provide a meso-scale network description of the relative inflexibility of specific large-scale brain networks during acute abstinence.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
The nicotine withdrawal syndrome (NWS) includes affective and cognitive disruptions whose incidence and severity vary across time during acute abstinence. However, most network-level neuroimaging uses static measures of resting-state functional connectivity and assumes time-invariance and is thus unable to capture dynamic brain-behavior relationships. Recent advances in resting-state functional connectivity signal processing allow characterization of time-varying functional connectivity (TVFC), which characterizes network communication between networks that reconfigure over the course of data collection. Therefore, TVFC may more fully describe network dysfunction related to the NWS.
Methods
To isolate alterations in the frequency and diversity of communication across network boundaries during acute nicotine abstinence, we scanned 25 cigarette smokers in the nicotine-sated and abstinent states and applied a previously validated method to characterize TVFC at a network and a nodal level within the brain.
Results
During abstinence, we found brain-wide decreases in the frequency of interactions between network nodes in different modular communities (i.e., temporal flexibility). In addition, within a subset of the networks examined, the variability of these interactions across community boundaries (i.e., spatiotemporal diversity) also decreased. Finally, within 2 of these networks, the decrease in spatiotemporal diversity was significantly related to NWS clinical symptoms.
Conclusions
Using multiple measures of TVFC in a within-subjects design, we characterized a novel set of changes in network communication and linked these changes to specific behavioral symptoms of the NWS. These reductions in TVFC provide a meso-scale network description of the relative inflexibility of specific large-scale brain networks during acute abstinence.
The nicotine withdrawal syndrome (NWS) includes affective and cognitive disruptions whose incidence and severity vary across time during acute abstinence. However, most network-level neuroimaging uses static measures of resting-state functional connectivity and assumes time-invariance and is thus unable to capture dynamic brain-behavior relationships. Recent advances in resting-state functional connectivity signal processing allow characterization of time-varying functional connectivity (TVFC), which characterizes network communication between networks that reconfigure over the course of data collection. Therefore, TVFC may more fully describe network dysfunction related to the NWS.
Methods
To isolate alterations in the frequency and diversity of communication across network boundaries during acute nicotine abstinence, we scanned 25 cigarette smokers in the nicotine-sated and abstinent states and applied a previously validated method to characterize TVFC at a network and a nodal level within the brain.
Results
During abstinence, we found brain-wide decreases in the frequency of interactions between network nodes in different modular communities (i.e., temporal flexibility). In addition, within a subset of the networks examined, the variability of these interactions across community boundaries (i.e., spatiotemporal diversity) also decreased. Finally, within 2 of these networks, the decrease in spatiotemporal diversity was significantly related to NWS clinical symptoms.
Conclusions
Using multiple measures of TVFC in a within-subjects design, we characterized a novel set of changes in network communication and linked these changes to specific behavioral symptoms of the NWS. These reductions in TVFC provide a meso-scale network description of the relative inflexibility of specific large-scale brain networks during acute abstinence.
2019
Flannery, Jessica S; Riedel, Michael C; Poudel, Ranjita; Laird, Angela R; Ross, Thomas J; Salmeron, Betty Jo; Stein, Elliot A; Sutherland, Matthew T
In: Sci Adv, vol. 5, no. 10, pp. eaax2084, 2019, ISSN: 2375-2548 (Electronic); 2375-2548 (Linking).
@article{Flannery:2019aa,
title = {Habenular and striatal activity during performance feedback are differentially linked with state-like and trait-like aspects of tobacco use disorder.},
author = {Jessica S Flannery and Michael C Riedel and Ranjita Poudel and Angela R Laird and Thomas J Ross and Betty Jo Salmeron and Elliot A Stein and Matthew T Sutherland},
url = {https://www.ncbi.nlm.nih.gov/pubmed/31633021},
doi = {10.1126/sciadv.aax2084},
issn = {2375-2548 (Electronic); 2375-2548 (Linking)},
year = {2019},
date = {2019-10-09},
urldate = {2019-10-09},
journal = {Sci Adv},
volume = {5},
number = {10},
pages = {eaax2084},
address = {Department of Psychology, Florida International University, Miami, FL, USA.},
abstract = {The habenula, an epithalamic nucleus involved in reward and aversive processing, may contribute to negative reinforcement mechanisms maintaining nicotine use. We used a performance feedback task that differentially activates the striatum and habenula and administered nicotine and varenicline (versus placebos) to overnight-abstinent smokers and nonsmokers to delineate feedback-related functional brain alterations both as a function of smoking trait (smokers versus nonsmokers) and drug administration state (drug versus placebo). Smokers showed less striatal responsivity to positive feedback, an alteration not mitigated by drug administration, but rather correlated with trait-level addiction severity. Conversely, nicotine administration reduced habenula activity following both positive and negative feedback among abstinent smokers, but not nonsmokers, and increased habenula activity among smokers correlated with elevated state-level tobacco cravings. These outcomes highlight a dissociation between neurobiological processes linked with the dependence severity trait and the nicotine withdrawal state. Interventions simultaneously targeting both aspects may improve currently poor cessation outcomes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The habenula, an epithalamic nucleus involved in reward and aversive processing, may contribute to negative reinforcement mechanisms maintaining nicotine use. We used a performance feedback task that differentially activates the striatum and habenula and administered nicotine and varenicline (versus placebos) to overnight-abstinent smokers and nonsmokers to delineate feedback-related functional brain alterations both as a function of smoking trait (smokers versus nonsmokers) and drug administration state (drug versus placebo). Smokers showed less striatal responsivity to positive feedback, an alteration not mitigated by drug administration, but rather correlated with trait-level addiction severity. Conversely, nicotine administration reduced habenula activity following both positive and negative feedback among abstinent smokers, but not nonsmokers, and increased habenula activity among smokers correlated with elevated state-level tobacco cravings. These outcomes highlight a dissociation between neurobiological processes linked with the dependence severity trait and the nicotine withdrawal state. Interventions simultaneously targeting both aspects may improve currently poor cessation outcomes.
2018
Ding, Xiaoyu; Salmeron, Betty Jo; Wang, Jamei; Yang, Yihong; Stein, Elliot A; Ross, Thomas J
Evidence of subgroups in smokers as revealed in clinical measures and evaluated by neuroimaging data: a preliminary study. Journal Article
In: Addict Biol, vol. 24, no. 4, pp. 777–786, 2018, ISSN: 1369-1600 (Electronic); 1355-6215 (Linking).
@article{Ding:2019aa,
title = {Evidence of subgroups in smokers as revealed in clinical measures and evaluated by neuroimaging data: a preliminary study.},
author = {Xiaoyu Ding and Betty Jo Salmeron and Jamei Wang and Yihong Yang and Elliot A Stein and Thomas J Ross},
url = {https://www.ncbi.nlm.nih.gov/pubmed/29516603},
doi = {10.1111/adb.12620},
issn = {1369-1600 (Electronic); 1355-6215 (Linking)},
year = {2018},
date = {2018-03-08},
urldate = {2018-03-08},
journal = {Addict Biol},
volume = {24},
number = {4},
pages = {777--786},
address = {Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA.},
abstract = {To date, fractionation of the nicotine addiction phenotype has been limited to that based primarily on characteristics of cigarette use, although it is widely appreciated that a variety of individual factors are associated with tobacco use disorder. Identifying subtypes of tobacco use disorder based on such factors may lead to better understanding of potential treatment targets, individualize treatments and improve outcomes. In this preliminary study, to identify potential subgroups, we applied hierarchical clustering to a broad range of assessments measuring personality, IQ and psychiatric symptoms, as well as various environmental and experiential characteristics from 102 otherwise healthy cigarette smokers. The identified subgroups were further compared on various resting-state fMRI measures from a subset (N = 65) of individuals who also underwent resting-state fMRI scanning. The clustering dendrogram indicated that smokers can be divided into three subgroups. Each subgroup had unique clinical assessment characteristics. The division yielded imaging differences between subgroups in the supplementary motor area/middle cingulate cortex and the cuneus. Regression analyses showed that amplitude of low frequency fluctuations in the supplementary motor area/middle cingulate cortex differed between groups and were negatively correlated with the Toronto Alexithymia Scale subscale Difficulty Describing Feelings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To date, fractionation of the nicotine addiction phenotype has been limited to that based primarily on characteristics of cigarette use, although it is widely appreciated that a variety of individual factors are associated with tobacco use disorder. Identifying subtypes of tobacco use disorder based on such factors may lead to better understanding of potential treatment targets, individualize treatments and improve outcomes. In this preliminary study, to identify potential subgroups, we applied hierarchical clustering to a broad range of assessments measuring personality, IQ and psychiatric symptoms, as well as various environmental and experiential characteristics from 102 otherwise healthy cigarette smokers. The identified subgroups were further compared on various resting-state fMRI measures from a subset (N = 65) of individuals who also underwent resting-state fMRI scanning. The clustering dendrogram indicated that smokers can be divided into three subgroups. Each subgroup had unique clinical assessment characteristics. The division yielded imaging differences between subgroups in the supplementary motor area/middle cingulate cortex and the cuneus. Regression analyses showed that amplitude of low frequency fluctuations in the supplementary motor area/middle cingulate cortex differed between groups and were negatively correlated with the Toronto Alexithymia Scale subscale Difficulty Describing Feelings.
2017
Ding, Xiaoyu; Yang, Yihong; Stein, Elliot A.; Ross, Thomas J.
Combining Multiple Resting-State fMRI Features during Classification: Optimized Frameworks and Their Application to Nicotine Addiction Journal Article
In: Frontiers in Human Neuroscience, vol. 11, pp. 362, 2017, ISSN: 1662-5161.
@article{dingCombiningMultipleRestingState2017,
title = {Combining Multiple Resting-State fMRI Features during Classification: Optimized Frameworks and Their Application to Nicotine Addiction},
author = {Xiaoyu Ding and Yihong Yang and Elliot A. Stein and Thomas J. Ross},
url = {https://pubmed.ncbi.nlm.nih.gov/28747877/},
doi = {10.3389/fnhum.2017.00362},
issn = {1662-5161},
year = {2017},
date = {2017-07-12},
urldate = {2017-07-12},
journal = {Frontiers in Human Neuroscience},
volume = {11},
pages = {362},
abstract = {Machine learning techniques have been applied to resting-state fMRI data to predict neurological or neuropsychiatric disease states. Existing studies have used either a single type of resting-state feature or a few feature types (less than 4) in the prediction model. However, resting-state data can be processed in many different ways, yielding different feature types containing complementary and/or novel information, leaving uncertain the most informative features to provide to the classifier. In this study, multiple resting-state features were calculated from two main analytical categories: local measures and network measures. Feature selection was adopted using an optimized grid-search approach selecting top ranked features from statistical tests. We then tested three optimized frameworks: feature combination, kernel combination, and classifier combination, all using the support vector machine as an elementary classifier, to combine these resting-state feature types. When applied to nicotine addiction, with a cohort size of 100 smokers and 100 nonsmokers, via a 10-fold cross-validation procedure, the feature combination and the classifier combination achieved an accuracy of 75.5%, while the kernel combination achieved a 73.0% accuracy; all three combination frameworks improved classification performance compared to the single feature type based results (best accuracy 70.5%). This study not only reveals the discriminative power of resting-state data, but also demonstrates the efficiency of combining multiple features from one data phenotype to improve classification performance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Machine learning techniques have been applied to resting-state fMRI data to predict neurological or neuropsychiatric disease states. Existing studies have used either a single type of resting-state feature or a few feature types (less than 4) in the prediction model. However, resting-state data can be processed in many different ways, yielding different feature types containing complementary and/or novel information, leaving uncertain the most informative features to provide to the classifier. In this study, multiple resting-state features were calculated from two main analytical categories: local measures and network measures. Feature selection was adopted using an optimized grid-search approach selecting top ranked features from statistical tests. We then tested three optimized frameworks: feature combination, kernel combination, and classifier combination, all using the support vector machine as an elementary classifier, to combine these resting-state feature types. When applied to nicotine addiction, with a cohort size of 100 smokers and 100 nonsmokers, via a 10-fold cross-validation procedure, the feature combination and the classifier combination achieved an accuracy of 75.5%, while the kernel combination achieved a 73.0% accuracy; all three combination frameworks improved classification performance compared to the single feature type based results (best accuracy 70.5%). This study not only reveals the discriminative power of resting-state data, but also demonstrates the efficiency of combining multiple features from one data phenotype to improve classification performance.
2015
Ding, Xiaoyu; Yang, Yihong; Stein, Elliot A; Ross, Thomas J
Multivariate classification of smokers and nonsmokers using SVM-RFE on structural MRI images. Journal Article
In: Hum Brain Mapp, vol. 36, no. 12, pp. 4869–4879, 2015, ISSN: 1097-0193 (Electronic); 1065-9471 (Linking).
@article{Ding2015,
title = {Multivariate classification of smokers and nonsmokers using SVM-RFE on structural MRI images.},
author = {Xiaoyu Ding and Yihong Yang and Elliot A Stein and Thomas J Ross},
url = {https://www.ncbi.nlm.nih.gov/pubmed/26497657},
doi = {10.1002/hbm.22956},
issn = {1097-0193 (Electronic); 1065-9471 (Linking)},
year = {2015},
date = {2015-10-24},
urldate = {2015-10-24},
journal = {Hum Brain Mapp},
volume = {36},
number = {12},
pages = {4869--4879},
address = {Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland.},
abstract = {Voxel-based morphometry (VBM) studies have revealed gray matter alterations in smokers, but this type of analysis has poor predictive value for individual cases, which limits its applicability in clinical diagnoses and treatment. A predictive model would essentially embody a complex biomarker that could be used to evaluate treatment efficacy. In this study, we applied VBM along with a multivariate classification method consisting of a support vector machine with recursive feature elimination to discriminate smokers from nonsmokers using their structural MRI data. Mean gray matter volumes in 1,024 cerebral cortical regions of interest created using a subparcellated version of the Automated Anatomical Labeling template were calculated from 60 smokers and 60 nonsmokers, and served as input features to the classification procedure. The classifier achieved the highest accuracy of 69.6% when taking the 139 highest ranked features via 10-fold cross-validation. Critically, these features were later validated on an independent testing set that consisted of 28 smokers and 28 nonsmokers, yielding a 64.04% accuracy level (binomial P = 0.01). Following classification, exploratory post hoc regression analyses were performed, which revealed that gray matter volumes in the putamen, hippocampus, prefrontal cortex, cingulate cortex, caudate, thalamus, pre-/postcentral gyrus, precuneus, and the parahippocampal gyrus, were inversely related to smoking behavioral characteristics. These results not only indicate that smoking related gray matter alterations can provide predictive power for group membership, but also suggest that machine learning techniques can reveal underlying smoking-related neurobiology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Voxel-based morphometry (VBM) studies have revealed gray matter alterations in smokers, but this type of analysis has poor predictive value for individual cases, which limits its applicability in clinical diagnoses and treatment. A predictive model would essentially embody a complex biomarker that could be used to evaluate treatment efficacy. In this study, we applied VBM along with a multivariate classification method consisting of a support vector machine with recursive feature elimination to discriminate smokers from nonsmokers using their structural MRI data. Mean gray matter volumes in 1,024 cerebral cortical regions of interest created using a subparcellated version of the Automated Anatomical Labeling template were calculated from 60 smokers and 60 nonsmokers, and served as input features to the classification procedure. The classifier achieved the highest accuracy of 69.6% when taking the 139 highest ranked features via 10-fold cross-validation. Critically, these features were later validated on an independent testing set that consisted of 28 smokers and 28 nonsmokers, yielding a 64.04% accuracy level (binomial P = 0.01). Following classification, exploratory post hoc regression analyses were performed, which revealed that gray matter volumes in the putamen, hippocampus, prefrontal cortex, cingulate cortex, caudate, thalamus, pre-/postcentral gyrus, precuneus, and the parahippocampal gyrus, were inversely related to smoking behavioral characteristics. These results not only indicate that smoking related gray matter alterations can provide predictive power for group membership, but also suggest that machine learning techniques can reveal underlying smoking-related neurobiology.
