Torres Protocols

The following protocols can be used to probe the nervous systems functioning and map the

levels of noise and randomness in the biophysical signals harnessed non-invasively from the

participant or patient. These protocols have been used to provide the patient and the physicians

with a comprehensive profile of the nervous systems before a given intervention, during the

intervention and after the intervention. They provide a variety of biometrics indicating levels of

voluntary control, levels of spontaneous function, levels of involuntary motions, levels of

automatic motions and levels of autonomic control. The basic tasks are illustrated below, but

each task has variants to probe the systems functioning in relation to baseline condition. They

take a total of 15min or can also be unfolded into a longer protocol 45-60min long.

Figure 1 Several tasks to execute a simple protocol that probes levels of control of the nervous systems proposed by Torres in (Torres 2011) (voluntary, volitional control using pointing and variants of full cycle deliberate forward to the target and spontaneous (consequential) retracting motions away from the target; automatic walking to measure gait and balance motor control, whereby the subject will have to turn or undergo various other condition; resting state to measure pollution of undesirable involuntary motions impeding proper volitional control to maintain the body still.

Figure 2. The simple pointing task (A) and the match to sample variant of the basic pointing task (B)

appeared in (Torres et al. 2013a; Torres et al. 2013b; Wu et al. 2018).

Repetitive Pointing Task: Instruct the participant to point to a target on the screen. We have used

touch screens to detect the touch and position sensors to study motion trajectories, their geometry

and their kinematics. The motions last on the order of 600-800ms in typical people and 1000-

1500ms in patients with pathologies. We have studied Autism, Parkinson’s disease,

deafferentation, stroke, schizophrenia and neurotypical development/aging using this basic

pointing task. The papers explaining variants of the task (including as well reach-to-grasp tasks)

to explore cognitive loads and decision making (embodied cognition) are: (autism vs. controls)

(Torres et al. 2013a; Torres et al. 2013b; Wu et al. 2018); Parkinson’s disease, essential tremor

and de-afferentation (Amano et al. 2015; Hong et al. 2013; Torres et al. 2014; Torres et al. 2011;

Yanovich et al. 2013); Schizophrenia (Nguyen et al. 2016; Nguyen et al. 2014b); parietal stroke

(visuomotor neglect) (Torres et al. 2010); typical perceptual and cognitive tasks (Kalmpratsidou

and Torres 2014; Nguyen et al. 2013; Nguyen et al. 2014a; Ryu and Torres 2018; Torres and

Zipser 2002; 2004); summary paper mapping out several disorders using the basic pointing task

(Torres et al. 2016a)

Other versions of the pointing task to explore cognitive loads and decision

making:

Instead of just one target (e.g. a green dot on the black background, vary the size of the

target (the size of the circle) to test speed accuracy trade off (Fitt’s law). As the circle size

decreases, the demands on accuracy puts a heavier load in the system and it is possible to

systematically quantify the effects on the movement trajectories.

Add a decision making component to the task, e.g. match to sample, whereby two choices

appear (they can be of any stimulus type) and at the touch of the screen, the sample to be

matched appears on the center of the screen. The subject has to decide on which of the 2

possible choice samples is the match to the sample that appeared on the center. This task

can be combined with memory probing by making the original two samples present

during the decision making or absent (the subject has to memorize them) or present partly

for some time period, etc. examples below (Torres and Andersen 2006; Torres et al.

2013c)

Add cognitive loads, e.g. systematic increase in task complexity (by increasing cognitive

demands such as counting while pointing, or having to recall a previously presented

stimulus, etc.) All these conditions affect the motor output and our biometrics capture the

shifts in stochastic trajectories that traditional “grand-averaging” methods miss. These

traditional methods smooth out the moment by moment fluctuations as “noise” and lose

the very information they should be probing. (cognitive loads were used in (Nguyen et al.

2015; Nguyen et al. 2014a; Ryu and Torres 2018)

Add perceptual condition to probe the system and select the best form of sensory

guidance (e.g. provide visual feedback from the target; provide visual feedback of the

moving hand but block the target feedback and move in the dark to a memorized target)

as in (Torres and Andersen 2006; Torres 2010; Torres et al. 2014; Torres et al. 2011;

Torres et al. 2013c; Torres et al. 2010)

Figure 3. Sample decision making hand trajectories from a variant of the pointing paradigm. Forward decisions are aimed to the target the person decides on (the choice) whereas backwards motions are upon the decision was made and the hand returns back to resting area (without being instructed to do so). This is a form of spontaneous motion discovered by Torres and reported in (Johnson et al. 2012a; b; Torres 2011; Torres et al. 2011; Torres et al. 2010) among others

Figure 4. This is a version of the pointing task that probes different forms of sensory guidance and provides motor criteria to select which form of guidance is the most effective. Full description appears in (Torres et al. 2014; Torres et al. 2011; Torres et al. 2010)

Figure 5. Probing different cognitive loads. Full explanation in (Ryu and Torres 2018) in two sets of

experiments that use the basic pointing task

The walking and turn (gait/balance) task

Figure 6. Walking on a platform lifted one foot from the floor to force the person to turn at the end of the platform and return back to the start. Several conditions can be added, such as walk on straight line along a marked path, walk around obstacles, lift the leg up to produce other more challenging motions, etc. Full paradigm described here (Torres et al. 2016b)

Figure 7. Walking plus protocol. The simple walking and turning paradigm augmented to probe levels of spontaneous entrainment of the biorhythms and external rhythms (e.g. metronome in your cell phone) without instructing participant (spontaneous entrainment) and comparing departure from automatic walking when instructing the participant to deliberately breath to the rhythms of the metronome. (A) The insoles to wear in regular shoes to flexibly adapt them to the person’s shoes (insole from the shoes come out and zeblok insoles go in). (B) Schematic of the walking plus paradigm. (C) Automatic separation of activities using the zeblok smart shoes. Published in (Ryu and Torres 2017; Torres et al. 2018)

Figure 8. Using the walking-plus task and electroencephalography (EEG) and electrocardiogram (ECG) and a grid of biosensors across the body to explore levels of automatic, spontaneous and deliberate control across the Central, Peripheral and Autonomic nervous systems (CNS, PNS, ANS). Unambiguous distinction between Autism (ASD) and Typical Development (TD). Published in (Ryu and Torres 2017)

Figure 9. Resting state test of involuntary motions while instructing patient to remain still. Map of bodily micro-movements and noise. Estimated probability distribution values of shape and dispersion across 3min divided into ½ minute blocks. Involuntary head motion biometrics published in (Caballero et al. 2018; Torres and Denisova 2016; Torres et al. 2017)

References

Amano S, Hong SL, Sage JI, and Torres EB. Behavioral inflexibility and motor dedifferentiation in persons with Parkinson's disease: bilateral coordination deficits during a unimanual reaching task. Neurosci Lett 585: 82-87, 2015.Caballero C, Mistry S, Vero J, and Torres EB. Characterization of Noise Signatures of Involuntary Head Motion in the Autism Brain Imaging Data Exchange Repository. Front Integr Neurosci 12: 7, 2018.Hong SL, Isenhower RW, Jose JV, and Torres EB. Cognitive load results in motor overflow in essential tremor. Neurocase 2013.Johnson G, Yanovich P, Difeo G, Yang L, Santos E, Ross N, and Torres EB. Congruent map between the kinesthetic and the visual perceptions of our physical movements, even with noise. In: Annual Meeting of the Society for Neuroscience. New Orleans, LA: 2012a.Johnson G, Yanovich P, Difeo G, Yang L, Santos E, Ross N, and Torres EB. What do we see in each other: How movement drives social interaction. In: IGERT-NSF Video and Poster Competition. Washington DC: Award Winning http://posterhall.org/igert2012/posters/220, 2012b.Kalmpratsidou V, and Torres EB. Invariant and variable relations emerge with degrees of difficulty within habitual and surprise touch-point motions. In: Visual Science Society Annual Meeting. Saint Pete's Beach, Tampa Bay, Fla: 2014.Nguyen J, Isenhower R, Yanovich P, Ravaliya J, Papathomas T, and Torres EB. Quantifying changes in the kinesthetic percept under a 3D perspective visual illusion. In: Vision Science Society. Naples, Fla: 2013.Nguyen J, Majmudar U, Papathomas TV, Silverstein SM, and Torres EB. Schizophrenia: The micro-movements perspective. Neuropsychologia 85: 310-326, 2016.Nguyen J, Majmudar UV, Ravaliya JH, Papathomas TV, and Torres EB. Automatically Characterizing Sensory-Motor Patterns Underlying Reach-to-Grasp Movements on a Physical Depth Inversion Illusion. Front Hum Neurosci 9: 694, 2015.Nguyen J, Papathomas T, Ravaliya J, and Torres EB. Methods to Explore the Influence of Top-Down Visual Processes on Motor Behavior. J of Vis Exp 2014a.Nguyen J, Silverstein SM, Papathomas TV, and Torres EB. Characterization of visuomotor behavior in patients with schizophrenia under a 3D-depth inversion illusion. In: The Annual Meeting of the Society for Neuroscience. Washington DC: 2014b.Ryu J, and Torres EB. Characterization of Sensory-Motor Behavior Under Cognitive Load Using a New Statistical Platform for Studies of Embodied Cognition. Front Hum Neurosci 12: 116, 2018.Ryu J, and Torres EB. Methods for Dynamically Coupled Brain Body Tracking. In: Fourth International Symposium on Movement and Computing, MOCO'17. London, UK: 2017, p. 1-8.Torres E, and Andersen R. Space-time separation during obstacle-avoidance learning in monkeys. J Neurophysiol 96: 2613-2632, 2006.Torres EB. New symmetry of intended curved reaches. Behav Brain Funct 6: 21, 2010.Torres EB. Two classes of movements in motor control. Exp Brain Res 215: 269-283, 2011.

Torres EB, Brincker M, Isenhower RW, Yanovich P, Stigler KA, Nurnberger JI, Metaxas DN, and Jose JV. Autism: the micro-movement perspective. Front Integr Neurosci 7: 32, 2013a.Torres EB, Cole J, and Poizner H. Motor output variability, deafferentation, and putative deficits in kinesthetic reafference in Parkinson's disease. Front Hum Neurosci 8: 823, 2014.Torres EB, and Denisova K. Motor noise is rich signal in autism research and pharmacological treatments. Sci Rep 6: 37422, 2016.Torres EB, Heilman KM, and Poizner H. Impaired endogenously evoked automated reaching in Parkinson's disease. J Neurosci 31: 17848-17863, 2011.Torres EB, Isenhower RW, Nguyen J, Whyatt C, Nurnberger JI, Jose JV, Silverstein SM, Papathomas TV, Sage J, and Cole J. Toward Precision Psychiatry: Statistical Platform for the Personalized Characterization of Natural Behaviors. Front Neurol 7: 8, 2016a.Torres EB, Isenhower RW, Yanovich P, Rehrig G, Stigler K, Nurnberger J, and Jose JV. Strategies to develop putative biomarkers to characterize the female phenotype with autism spectrum disorders. J Neurophysiol 110: 1646-1662, 2013b.Torres EB, Mistry S, Caballero-Sanchez C, and Whyatt CP. Stochastic signatures of involuntary head micro-movements can be used to classify females of ABIDE into different subtypes of 3 neurodevelopmental disorders. Frontiers in Integrative Neuroscience 11: 1-17, 2017.Torres EB, Nguyen J, Mistry S, Whyatt C, Kalampratsidou V, and Kolevzon A. Characterization of the Statistical Signatures of Micro-Movements Underlying Natural Gait Patterns in Children with Phelan McDermid Syndrome: Towards Precision-Phenotyping of Behavior in ASD. Front Integr Neurosci 10: 22, 2016b.Torres EB, Quian Quiroga R, Cui H, and Buneo CA. Neural correlates of learning and trajectory planning in the posterior parietal cortex. Front Integr Neurosci 7: 39, 2013c.Torres EB, Raymer A, Gonzalez Rothi LJ, Heilman KM, and Poizner H. Sensory-spatial transformations in the left posterior parietal cortex may contribute to reach timing. J Neurophysiol 104: 2375-2388, 2010.Torres EB, Vero J, and Rai R. Statistical Platform for Individualized Behavioral Analyses Using Biophysical Micro-Movement Spikes. Sensors (Basel) 18: 2018.Torres EB, and Zipser D. Reaching to grasp with a multi-jointed arm. I. Computational model. J Neurophysiol 88: 2355-2367, 2002.Torres EB, and Zipser D. Simultaneous control of hand displacements and rotations in orientation-matching experiments. J Appl Physiol (1985) 96: 1978-1987, 2004.Wu D, Jose JV, Nurnberger JI, and Torres EB. A Biomarker Characterizing Neurodevelopment with applications in Autism. Sci Rep 8: 614, 2018.Yanovich P, Isenhower RW, Sage J, and Torres EB. Spatial-orientation priming impedes rather than facilitates the spontaneous control of hand-retraction speeds in patients with Parkinson's disease. PLoS ONE 8: 1-19, 2013.