Brain Network Imaging and Brain Stimulation. Michael D. Fox, MD, PhD

Brain Network Imaging and Brain Stimulation Michael D. Fox, MD, PhD Director, Laboratory for Brain Network Imaging and Visualization Associate Director, Berenson-Allen Center for Noninvasive Brain Stimulation Associate Director, Deep Brain Stimulation Program Beth Israel Deaconess Medical Center, Harvard Medical School Neuroscientist, Department of Neurology and Martinos Center for Biomedical Imaging Massachusetts General Hospital, Harvard Medical School

Disclosures Intellectual property using connectivity imaging to guide brain stimulation (no royalties)

Outline Intro to brain network imaging What can network imaging do for brain stimulation? What can brain stimulation do for brain networks?

Types of Brain Network Imaging Co-activation Patterns Resting state functional connectivity MRI (Rs-fcMRI) Diffusion tensor imaging (DTI)

% BOLD Change 3 2.5 2 1.5 1 0.5 0-0.5-1 Classical Neuroimaging Open Open Open Open Closed Closed Closed Closed 0 50 100 150 200 250 Time (s) Open Closed = Fox and Raichle (2007) Nat. Rev. Neuro.

% BOLD Change 3 2.5 2 1.5 1 0.5 0-0.5-1 BOLD Data Is Very Noisy Open Open Open Open Closed Closed Closed Closed 0 50 100 150 200 250 Time (s) Open Closed = Fox and Raichle (2007) Nat. Rev. Neuro.

% BOLD Change 2 1.5 1 0.5 0-0.5-1 -1.5 Spontaneous Fluctuations ( Noise ) in the BOLD Signal Left Motor Cortex 0 50 100 150 200 250 300 Time (sec)

% BOLD Change 2 1.5 1 0.5 0-0.5-1 -1.5 Spontaneous Fluctuations are Specifically Correlated Left Motor Cortex Right Motor Cortex 0 50 100 150 200 250 300 Time (sec) After Bharat Biswal and colleagues (1995) Magnetic Resonance in Medicine

Generation of Resting State Functional Connectivity Maps % BOLD Change 2 1.5 1 0.5 0 0-0.5 50 100 150 200 250 300-1 Time (sec) -1.5 Fox and Raichle (2007) Nat. Rev. Neuro.

Generation of Resting State Functional Connectivity Maps Z score, fixed effects, N = 10 % BOLD Change 2 1.5 1 0.5 0 0-0.5 50 100 150 200 250 300-1 Time (sec) -1.5 Fox and Raichle (2007) Nat. Rev. Neuro.

% BOLD Change 1.5 1 0.5 0-0.5-1 -1.5-2 0 50 100 150 200 250 300 Time (sec)

% BOLD Change 1.5 1 0.5 0-0.5-1 -1.5-2 0 50 100 150 200 250 300 Time (sec) Fox et al. (2005) PNAS

% BOLD Change 1.5 1 0.5 0-0.5-1 -1.5-2 Task-induced changes negative positive 0 50 100 150 200 250 300 Time (sec) Fox et al. (2005) PNAS

Diffusion Tractography Fox et al. 2014 PNAS

Results match anatomical connectivity relevant to DBS response Fox et al. PNAS In Press

DTI Network Rs-fcMRI Network Honey et al. 2009 PNAS

Research Applications of Rs-fcMRI Trial to trial variability in behavior (Fox et al. 2007 Neuron) Thalamic and cerebellar connections (Zhang et al. 2009 J. Neurophys, Buckner et al. 2011 J. Neurophys.) Individual differences in performance (Hampson et al. 2006 J. Neurosci, Koyama et al. 2009 J. Neurosci.) Correlates of learning (Lewis et al. 2009 PNAS)

Clinical Applications of Rs-fcMRI Understanding disease pathophysiology Biomarkers / Diagnosis Guiding treatment

Understanding Peduncular Hallucinosis Boes et al. 2015 Brain

Disease/Condition References Findings Alzheimer s (Allen et al. 2007; Greicius et al. 2004; Li et al. 2002; Supekar et al. 2008; Wang et al. 2006a; Wang et al. 2007; Wang et al. Decreased correlations within the default mode network including hippocampi and decreased anticorrelations between the DMN and TPN 2006b) PIB positive (Hedden et al. 2009; Sheline et al. 2009) Decreased correlations within the default mode network Mild Cognitive Impairment (Li et al. 2002; Sorg et al. 2007) Decreased correlations within the default mode network and decreased anticorrelations between the DMN and TPN Fronto-Temporal Dementia (Seeley et al. 2007a; Seeley et al. 2008) Decreased correlations within the salience network Healthy Aging (Andrews-Hanna et al. 2007; Damoiseaux et al. 2007) Decreased correlations within the default mode network Multiple Sclerosis (De Luca et al. 2005; Lowe et al. 2002) Decreased correlations within the somatomotor network ALS (Mohammadi et al. 2009) Decreased connectivity in DMN and premotor cortex Depression (Anand et al. 2009; Anand et al. 2005a; b; Bluhm et al. 2009a; Greicius et al. 2007) Variable: Decreased connectivity between dacc and limbic regions (amygdala, medial thalamus, pallidostriatum) increased connectivity within the DMN (esp. subgenual prefrontal cortex), decreased connectivity between DMN and caudate Bipolar (Anand et al. 2009) Decreased corticolimbic connectivity PTSD (Bluhm et al. 2009c) Decreased connectivity in the DMN Schizophrenia (Bluhm et al. 2007; Bluhm et al. 2009b; Jafri et al. 2008; Liang et al. 2006; Liu et al. 2006; Liu et al. 2008; Salvador et al. 2007; Whitfield-Gabrieli et al. 2009; Zhou et al. 2007) Variable: Decreased or increased DMN connectivity Schizophrenia 1 relatives (Whitfield-Gabrieli et al. 2009) Increased connectivity in the DMN ADHD (Cao et al. 2006; Castellanos et al. 2008; Tian et al. 2006; Wang et al. 2008; Zang et al. 2007; Zhu et al. 2008; Zhu et al. Variable: reduced connectivity within the DMN, reduced anticorrelations, increased connectivity in salience 2005) Autism (Cherkassky et al. 2006; Kennedy and Courchesne 2008; Monk et al. 2009; Weng et al. 2009) Decreased connectivity within the DMN (although hippocampus is variable and connectivity may be increased in younger patients) Tourette Syndrome (Church et al. 2009) Delayed maturation of task-control and cingulo-opercular networks Epilepsy (Bettus et al. 2009; Lui et al. 2008; Waites et al. 2006; Zhang et al. 2009a; Zhang et al. 2009b) Variable: decreased connectivity in mult. networks including medial temporal lobe, decreased connectivity in DMN with generalized seizure Blindness (Liu et al. 2007; Yu et al. 2008) decreased connectivity within the visual cortices and between visual cortices and somatosensory, frontal motor and temporal multisensory cortices Chronic Pain (Cauda et al. 2009a; Cauda et al. 2009c; Cauda et al. 2009d; Variable: Increased/decreased connectivity within the salience network, decreased Greicius et al. 2008) connectivity in attention networks Neglect (He et al. 2007) Decreased connectivity within the dorsal and ventral attention networks Vegetative State (Boly et al. 2009; Cauda et al. 2009b) Progressively decreased DMN connectivity with progressive states of impaired consciousness Fox and Greicius (2010) Frontiers Sys Neurosci

Outline Intro to brain network imaging What can network imaging do for brain stimulation? What can brain stimulation do for brain networks?

Therapeutic Brain Stimulation Deep Brain Stimulation (DBS) Implanted by Neurosurgeon Constant stimulation 130-180 Hz FDA approved for Parkinson s, essential tremor, dystonia, OCD Transcranial Magnetic Stimulation (TMS) Noninvasive Repeated sessions of stimulation 10 Hz (excitatory), 1Hz (inhibitory) FDA approved for depression

Therapeutic Brain Stimulation Deep Brain Stimulation (DBS) Transcranial Magnetic Stimulation (TMS) Both are showing early signs of utility in many of the same disorders

Disease Invasive (DBS) Noninvasive (TMS, tdcs) Addiction NA DLPFC (laterality unclear) Alzheimer s Fornix Bilateral DLPFC (+/- parietal, temporal) Anorexia NA, Subgenual L DLPFC Depression Subgenual, VC/VS, NA, MFB, habenula Left DLPFC, R DLPFC Dystonia GPi SMA/ACC, Premotor Epilepsy Essential Tremor Thalamus (AN, CM), MTL VIM Active EEG focus Cerebellum Midline Cerebellum, Lateral Cerebellum, M1 Gait Dysfunction PPN M1 (leg area) Huntington s GPi SMA Minimally Conscious Thalamus (intralaminar/cl, CM/Pf) R DLPFC, M1 Obsessive Compulsive Disorder VC/VS, NA, ALIC, STN L orbitofrontal, Pre-SMA Pain PAG, Thalamus (VPL/VPM) M1 Parkinson s STN, GPi M1, SMA Tourette s Thalamus (CM/Pf), GPi, NA, ALIC SMA Fox et al. 2014 PNAS

Therapeutic Brain Stimulation Deep Brain Stimulation (DBS) Transcranial Magnetic Stimulation (TMS) Both propagate beyond the site of stimulation to impact a distributed network of brain regions

TMS propagates trans-synaptically

TMS propagates trans-synaptically Fox et al. 2012 Neuroimage

Guiding Logic 1. Both techniques are useful in many of the same diseases 2. Both techniques propagate through anatomical connections to impact distributed brain networks

Guiding Logic 1. Both techniques are useful in many of the same diseases 2. Both techniques propagate through anatomical connections to impact distributed brain networks Are both techniques targeting the same network?

BOLD Signal (% change) 1.5 1.0 0.5 0.0-0.5-1.0-1.5 Subgenual Seed 0 50 100 150 200 250 300 350 Time (sec) Fox et al. 2012 Biol Psych.

Invasive and Noninvasive Brain Stimulation Sites are Linked Across 14 Diseases Fox et al. 2014 PNAS

Invasive and Noninvasive Brain Stimulation Sites are Linked Across 14 Diseases DBS Correlation (r) 0.1 0.08 0.06 0.04 0.02 P < 0.005 0 Best Noninvasive Stimulation Site Random Noninvasive Stimulation Sites Fox et al. 2014 PNAS

Ineffective sites are characterized by an absence of functional connectivity Parkinson s Disease Pain Fox et al. 2014 PNAS Essential Tremor Depression

The sign of the correlation (positive vs negative) relates to the reported utility of excitatory vs inhibitory stimulation Fox et al. 2014 PNAS

Can we take advantage of network imaging to improve brain stimulation?

Targeting TMS in Depression: The 5 cm method Herwig et al. 2001 BIOL PSYCHIATRY 50:58 61

Targeting TMS in Depression: The 5 cm method Only hit the DLPFC ~40% of the time Herwig et al. 2001 BIOL PSYCHIATRY 50:58 61

TMS targets vary in their efficacy Herbsman et al. 2009 Ineffective Effective 18% responders 42% responders Fitzgerald et al. 2009

Hypothesis: More effective TMS targets show stronger connectivity to the subgenual than less effective targets

Subgenual Correlation (r) 0.00-0.05-0.10-0.15 Effective vs. Ineffective TMS Targets vs More Effective 5cm Less Effective 5cm Fitzgerald Target Avg. 5cm Target P < 0.005 Subgenual Correlation (r) 0.00-0.10-0.20-0.30 vs P < 5 x 10-8 Fox et al. 2012 Biol Psych.

Effective vs. Ineffective TMS Targets vs More Effective 5cm Less Effective 5cm Fitzgerald Target Avg. 5cm Target 6 15 0-6 -15 vs Fox et al. 2012 Biol Psych. 0

Effective vs. Ineffective TMS Targets

Optimizing the TMS target for depression Subgenual Seed Efficacy-based Seed Map Fox et al. 2012 Biol Psych.

DLPFC Left DLPFC connectivity is highly variable between subjects 0.53 0.73 Mueller and Liu et al. Neuron 2013

Individualized TMS targets for depression Subgenual Seed Efficacybased Seed Map Fox et al. 2012 Neuroimage

Individual differences in functional connectivity are reproducible across days Subgenual Seed Efficacybased Seed Map Fox et al. 2012 Neuroimage

Can we predict individual patient responses to TMS?

Why pick just one site? (+) (-)

Why pick just one site? Ruffini, Fox et al. 2014 Neuroimage

Outline Intro to brain network imaging What can network imaging do for brain stimulation? What can brain stimulation do for brain networks?

Eldaief et al. 2012 PNAS

Conclusions Brain stimulation propagates through brain networks Network imaging can help us understand and guide brain stimulation Brain stimulation might be used to modify connectivity in brain networks altered by disease

Marc Raichle, Avi Snyder Mike Greicius Acknowledgements Alvaro Pascual-Leone, Aaron Boes, Anne Weigand, Simon Laganiere, David Fischer Randy Buckner, Hesheng Liu, Justin Vincent, Tianyi Qian, Sophia Mueller, Verne Caviness Andres Lozano Giulio Ruffini Mallar Chakravarty Sashank Prasad Funding NINDS (R25, K23) NIMH (R21) AAN / ABF Sidney Baer Foundation

Questions? Contact: foxmdphd@gmail.com