Neuronal activity in the substantia nigra in the anaesthetized rat has fractal characteristics. Evidence for firing-code patterns in the basal ganglia

Rodrı́guez, Manuel; Pereda, Ernesto; González, José M.; Abdala, Patricio; Obeso, José A.

doi:10.1007/s00221-003-1442-4

Cited by 27 publications

(19 citation statements)

References 32 publications

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“…In attempts to move beyond traditional measures of neural coding and employ advanced tools from the signal processing world, spike trains have been analyzed for entropy (Strong et al, 1998) and fractal structure (Rodriquez et al, 2003; Goldberger et al, 2002). These measures have been used to explore the effects of apomorphine in Parkinson's patients (Rasouli et al, 2006), DBS in parkinsonian non-human primates (Dorval et al, 2008), and dopamine depletion in rats (Cruz et al, 2009).…”

Section: Spike Train Analysesmentioning

confidence: 99%

“…Entropy is an indication of variability, based on the concept that some amount of variability in a time series is necessary to encode information (Strong et al, 1998; de Ruyter van Steveninck et al, 1997). Fractal dimension is an indication of the mathematical property of self-similarity, scale invariance or long range correlation (Rodriquez et al, 2003; Goldberger et al, 2002). The scaling exponent obtained in fractal analysis is an indication of the statistical properties or variability of a spike train.…”

Section: Spike Train Analysesmentioning

confidence: 99%

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Network perspectives on the mechanisms of deep brain stimulation

McIntyre

Hahn

2010

Neurobiology of Disease

410

274

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Deep brain stimulation (DBS) is an established medical therapy for the treatment of movement disorders and shows great promise for several other neurological disorders. However, after decades of clinical utility the underlying therapeutic mechanisms remain undefined. Early attempts to explain the mechanisms of DBS focused on hypotheses that mimicked an ablative lesion to the stimulated brain region. More recent scientific efforts have explored the wide-spread changes in neural activity generated throughout the stimulated brain network. In turn, new theories on the mechanisms of DBS have taken a systems-level approach to begin to decipher the network activity. This review provides an introduction to some of the network based theories on the function and pathophysiology of the cortico-basal-ganglia-thalamo-cortical loops commonly targeted by DBS. We then analyze some recent results on the effects of DBS on these networks, with a focus on subthalamic DBS for the treatment of Parkinson's disease. Finally we attempt to summarize how DBS could be achieving its therapeutic effects by overriding pathological network activity.

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Section: Spike Train Analysesmentioning

confidence: 99%

Section: Spike Train Analysesmentioning

confidence: 99%

Network perspectives on the mechanisms of deep brain stimulation

McIntyre

Hahn

2010

Neurobiology of Disease

410

274

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“…These states were shown not to be independent but, rather, linked by physical mechanisms that result in the observed fractal scaling. The impact of these findings can be appreciated from the profile of succeeding studies [85][86][87][88][89] on fractal kinetics from 1991 onwards. For example, in a recent work [89] it has been proved that patterns in spiking activity in suprachiasmatic nucleus neurons can be explained with the fractal point process model, which implies the presence of self-similarity in the profile of spike trains, proving thereby that no characteristic time scale dominates the dynamics of the spiking process.…”

Section: Fd-based Ion-channel Kinetics Studiesmentioning

confidence: 99%

Fractal symmetry of protein interior: what have we learned?

Banerji

Ghosh

2011

Cell. Mol. Life Sci.

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The application of fractal dimension-based constructs to probe the protein interior dates back to the development of the concept of fractal dimension itself. Numerous approaches have been tried and tested over a course of (almost) 30 years with the aim of elucidating the various facets of symmetry of self-similarity prevalent in the protein interior. In the last 5 years especially, there has been a startling upsurge of research that innovatively stretches the limits of fractal-based studies to present an array of unexpected results on the biophysical properties of protein interior. In this article, we introduce readers to the fundamentals of fractals, reviewing the commonality (and the lack of it) between these approaches before exploring the patterns in the results that they produced. Clustering the approaches in major schools of protein self-similarity studies, we describe the evolution of fractal dimension-based methodologies. The genealogy of approaches (and results) presented here portrays a clear picture of the contemporary state of fractal-based studies in the context of the protein interior. To underline the utility of fractal dimension-based measures further, we have performed a correlation dimension analysis on all of the available non-redundant protein structures, both at the level of an individual protein and at the level of structural domains. In this investigation, we were able to separately quantify the self-similar symmetries in spatial correlation patterns amongst peptide-dipole units, charged amino acids, residues with the π-electron cloud and hydrophobic amino acids. The results revealed that electrostatic environments in the interiors of proteins belonging to 'α/α toroid' (all-α class) and 'PLP-dependent transferase-like' domains (α/β class) are highly conducive. In contrast, the interiors of 'zinc finger design' ('designed proteins') and 'knottins' ('small proteins') were identified as folds with the least conducive electrostatic environments. The fold 'conotoxins' (peptides) could be unambiguously identified as one type with the least stability. The same analyses revealed that peptide-dipoles in the α/β class of proteins, in general, are more correlated to each other than are the peptide-dipoles in proteins belonging to the all-α class. Highly favorable electrostatic milieu in the interiors of TIM-barrel, α/β-hydrolase structures could explain their remarkably conserved (evolutionary) stability from a new light. Finally, we point out certain inherent limitations of fractal constructs before attempting to identify the areas and problems where the implementation of fractal dimension-based constructs can be of paramount help to unearth latent information on protein structural properties.

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“…Although MCA has been successfully implemented in different disciplines including psychology and health sciences (Bouilland and Loslever, 1998; Guinot et al, 2002; Meyer et al, 2004; Ambrogi et al, 2005; Almeida et al, 2009; Rennie and Roberts, 2009; Pinti et al, 2010; Avolio et al, 2013; Costa et al, 2013; Sagawa et al, 2013; Ayele et al, 2014; Touso et al, 2014), it is seldom encountered in neuroimaging literature. This method was applied here to study the functional connectivity of BG, centers which have shown multiple non-linear interactions in both animal and human studies (Rodriguez et al, 2003a,b; Marceglia et al, 2006; Schroll and Hamker, 2013). …”

Section: Introductionmentioning

confidence: 99%

The Multiple Correspondence Analysis Method and Brain Functional Connectivity: Its Application to the Study of the Non-linear Relationships of Motor Cortex and Basal Ganglia

et al. 2017

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The complexity of basal ganglia (BG) interactions is often condensed into simple models mainly based on animal data and that present BG in closed-loop cortico-subcortical circuits of excitatory/inhibitory pathways which analyze the incoming cortical data and return the processed information to the cortex. This study was aimed at identifying functional relationships in the BG motor-loop of 24 healthy-subjects who provided written, informed consent and whose BOLD-activity was recorded by MRI methods. The analysis of the functional interaction between these centers by correlation techniques and multiple linear regression showed non-linear relationships which cannot be suitably addressed with these methods. The multiple correspondence analysis (MCA), an unsupervised multivariable procedure which can identify non-linear interactions, was used to study the functional connectivity of BG when subjects were at rest. Linear methods showed different functional interactions expected according to current BG models. MCA showed additional functional interactions which were not evident when using lineal methods. Seven functional configurations of BG were identified with MCA, two involving the primary motor and somatosensory cortex, one involving the deepest BG (external-internal globus pallidum, subthalamic nucleus and substantia nigral), one with the input-output BG centers (putamen and motor thalamus), two linking the input-output centers with other BG (external pallidum and subthalamic nucleus), and one linking the external pallidum and the substantia nigral. The results provide evidence that the non-linear MCA and linear methods are complementary and should be best used in conjunction to more fully understand the nature of functional connectivity of brain centers.

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Neuronal activity in the substantia nigra in the anaesthetized rat has fractal characteristics. Evidence for firing-code patterns in the basal ganglia

Cited by 27 publications

References 32 publications

Network perspectives on the mechanisms of deep brain stimulation

Network perspectives on the mechanisms of deep brain stimulation

Fractal symmetry of protein interior: what have we learned?

The Multiple Correspondence Analysis Method and Brain Functional Connectivity: Its Application to the Study of the Non-linear Relationships of Motor Cortex and Basal Ganglia

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