Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Statistics for Spatial Data.

The majority of functional neuroscience studies have focused on the brain's response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. In this Article we review recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity. Although several challenges remain, these studies have provided insight into the intrinsic functional architecture of the brain, variability in behaviour and potential physiological correlates of neurological and psychiatric disease.

/pdf/spontaneous-fluctuations-in-brain-activity-observed-with-32z1nyfc7n.pdf

Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging.

Exercise Physiology: Energy, Nutrition and Human Performance

The relationship between functional MRI (fMRI)-measured brain signal and muscle force and or electromyogram (EMG) is critical in interpreting fMRI data and understanding the control mechanisms of voluntary motor actions. We designed a system that could record joint force and surface EMG online with fMRI data. High-quality force and EMG data were obtained while maintaining the quality of the fMRI brain images. Using this system, we determined the relationship between fMRI-measured brain activation and handgrip force and between fMRI-measured brain signal and EMG of extrinsic finger muscles. Ten volunteers participated in the experiments (only seven subjects' data were analyzed due to excessive noise in the fMRI data of three subjects). The participants exerted 20%, 35%, 50%, 65%, and 80% of the maximal force. During each contraction period, handgrip force, surface EMG of the finger flexor and extensor muscles, and fMRI brain images were acquired. The degree of muscle activation (force and EMG) was directly proportional to the amplitude of the brain signal determined by fMRI in the entire brain and in a number of motor function-related cortical fields, including primary motor, sensory regions, supplementary motor area, premotor, prefrontal, parietal and cingulate cortices, and cerebellum. All the examined brain areas demonstrated a similar relationship between the fMRI signal and force. A stronger fMRI signal during higher force indicates that more cortical output neurons and/or interneurons may participate in generating descending commands and/or processing additional sensory information. The similarity in the relationship between muscle output and fMRI signal in the cortical regions suggests that correlated or networked activation among a number of cortical fields may be necessary for controlling precise static force of finger muscles.

Relationship between muscle output and functional MRI-measured brain activation.

OBJECTIVES: The purpose of this study was to quantify age-induced changes in handgrip and finger-pinch strength, ability to maintain a steady submaximal finger pinch force and pinch posture, speed in relocating small objects with finger grip, and ability to discriminate two identical mechanical stimuli applied to the finger tip.

DESIGN: A cross-sectional study.

SETTINGS: Greater Cleveland area of Ohio.

PARTICIPANTS: Healthy, independent, young (n = 27, range 20–35 years) and older (n = 28, range 65–79 years) subjects.

MEASUREMENTS: Handgrip strength, maximum pinch force (MPF), ability to maintain a steady pinch force at three relative force levels (5%, 10%, and 20% MPF) and three absolute force levels (2.5 Newtons (N), 4 N, and 8 N), ability to maintain a precision pinch posture, speed in relocating pegs from a nearby location onto the pegboard, and the shortest distance for discriminating two stimuli were measured in both young and older groups.

RESULTS: Compared with young subjects, the older group's handgrip force was 30% weaker (P < .001), MPF was 26% lower (P < .05), and ability to maintain steady submaximal pinch force and a precision pinch posture was significantly less (P < .05). The time taken to relocate the pegs and the distance needed to discriminate two identical stimuli increased significantly with age (P < .01). The decrease in the ability to maintain steady submaximal pinch force was more pronounced in women than men.

CONCLUSION: Aging has a degenerative effect on hand function, including declines in hand and finger strength and ability to control submaximal pinch force and maintain a steady precision pinch posture, manual speed, and hand sensation.

Effects of aging on hand function

From mental power to muscle power—gaining strength by using the mind

During prolonged submaximal muscle contractions, electromyographic (EMG) signals typically increase as a result of increasing motor unit activities to compensate for fatigue-induced force loss in the muscle. It is thought that cortical signals driving the muscle to higher activation levels also increases, but this has never been experimentally demonstrated. The purpose of this study was to quantify brain activation during submaximal fatigue muscle contractions using functional magnetic resonance imaging (fMRI). Twelve volunteers performed a sustained handgrip contraction for 225 s and 320 intermittent handgrip contractions ( approximately 960 s) at 30% maximal level while their brain was imaged. For the sustained contraction, EMG signals of the finger flexor muscles increased linearly while the target force was maintained. The fMRI-measured cortical activities in the contralateral sensorimotor cortex increased sharply during the first 150 s, then plateaued during the last 75 s. For the intermittent contractions, the EMG signals increased during the first 660 s and then began to decline, while the handgrip force also showed a sign of decrease despite maximal effort to maintain the force. The fMRI signal of the contralateral sensorimotor area showed a linear rise for most part of the task and plateaued at the end. For both the tasks, the fMRI signals in the ipsilateral sensorimotor cortex, prefrontal cortex, cingulate gyrus, supplementary motor area, and cerebellum exhibited steady increases. These results showed that the brain increased its output to reinforce the muscle for the continuation of the performance and possibly to process additional sensory information.

https://www.physiology.org/doi/pdf/10.1152/jn.00821.2002

Human Brain Activation During Sustained and Intermittent Submaximal Fatigue Muscle Contractions: An fMRI Study

The purpose of this study was to investigate the relationship between EEG-derived motor activity-related cortical potential (MRCP) and voluntary muscle activation. Eight healthy volunteers participated in two experimental sessions. In one session, subjects performed isometric elbow-flexion contractions at four intensity levels [10%, 35%, 60%, and 85% maximal voluntary contraction (MVC)]. In another session, a given elbow-flexion force (35% MVC) was generated at three different rates (slow, intermediate, and fast). Thirty to 40 contractions were performed at each force level or rate. EEG signals were recorded from the scalp overlying the supplementary motor area (SMA) and contralateral sensorimotor cortex, and EMG signals were recorded from the skin surface overlying the belly of the biceps brachii and brachioradialis muscles during all contractions. In each trial, the force was used as the triggering signal for MRCP averaging. MRCP amplitude was measured from the beginning to the peak of the negative slope. The magnitude of MRCP from both EEG recording locations (sensorimotor cortex and SMA) was highly correlated with elbow-flexion force, rate of rising of force, and muscle EMG signals. These results suggest that MRCP represents cortical motor commands that scale the level of muscle activation.

Guang H. Yue

Papers

Relationship between muscle output and functional MRI-measured brain activation.

Effects of aging on hand function

From mental power to muscle power—gaining strength by using the mind

Human Brain Activation During Sustained and Intermittent Submaximal Fatigue Muscle Contractions: An fMRI Study

Relationship between motor activity-related cortical potential and voluntary muscle activation.