Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5234_Библиотеки_им_академика_М_И_Перельмана
.pdf
24 Computational Intelligence Algorithms
FIGURE 2.2 Neural engineers and the professional chain [8].
Some examples of neural engineering and neuromodulation include [11, 12]:
1. Deep brain stimulation (DBS): electrodes that are implanted in the body
to manipulate signals transmitted from the brain; used as a treatment for
Parkinson’s disease, dystonia, and obsessive-compulsive disorder.
2. Transcranial magnetic stimulation (TMS): a procedure that involves the
use of magnets to inuence activities of desired areas in the brain to cure
depression, anxiety, and chronic pain.
3. BCIs: Allow patients with paralysis, neural disorders, or tetraplegia, who
could not speak or manipulate anything, to use their minds to command
devices and participate in social activities.
4. Neuroprosthetics: prosthetics such as articial limbs and organs that could
be operated through signals from the brain to enable those who have had
their limbs or organs amputated or damaged to have their limbs or organs
replaced.
5. Neurofeedback training: forms mental and neural pathways, which increases
attention, memory, and overall intellectual performance.
6. Optogenetics: exploits light to manipulate granulocyte-macrophage (GM)
brain cells, map the mind/brain, and inuence behaviors.
FIGURE 2.3 Neurotechnology developmental cycle [13].
7. Neural dust: innovative nanobots inserted into the human skull that record
the brain’s signals and may cure brain-related diseases.
8. Graphene-based BCIs: semi- and fully implantable systems that can record
and interpret signals from the brain so that devices can be operated with a
high degree of accuracy.
25 Brain Challenges and Opportunities in Computational Neurology
All of these progressive technologies can bring significant change to the
healthcare industry, act socially beneficial by positively affecting the patient’s
standard of living, and alter the human condition for the better. Figure 2.3
explains about the developmental cycle of the neurotechnology process.
2.5 TOWARD PERSONALIZED MEDICINE IN NEUROLOGY
Personalized medicine in neurology is dened as the practice of providing medical
treatment concerning a patient’s genetic, environmental, and social individuality.
Some key aspects include [14, 15]:
• Genomic medicine: in its simplest form, utilizing genetic information to
forecast susceptibility to diseases and the likely individual response to
treatment
• Precision neurotherapeutics: an individualized approach to treatment using
the possibilities of pharmacogenomics to create highly specic treatments
for certain dysfunctions in the patient’s brain
• Pharmacogenomics: changing or modifying the type of drugs given depending on the patient’s genetic makeup
• BCIs: designing neural interfaces to be personalized to the point that one
can optimize their use for communication as well as control
26 Computational Intelligence Algorithms
• Neuroimaging biomarkers: to develop imaging markers that would dene
the disease early and help monitor the effectiveness of the treatment modalities employed
• Personalized neurorehabilitation: assuming individual rehabilitation
strategies by checking with the aficted segment of the brain and typical
demeanor
• Lifestyle medicine: personalizing nutrition, exercise, and other aspects
of a lifestyle according to a person’s genetic makeup and functions of
the brain
Applications and uses of key aspects that indicate personalized medicine in neu-
rology include:
1. Genomic medicine: introducing genetics to help identify tendencies and
reactions to intervention in diseases.
2. Precision neurotherapeutics: the service provision that would entail the
application of specialized pharmacology that would in effect be targeted
toward treatment that in most cases is concerned with specic aspects of a
given patient’s brain.
3. Pharmacogenomics: employing the reaction that occurs at the genetic
level of an individual using pharmacogenomic and pharmacogenetic
testing to establish which drug is appropriate for the patient or the most
suitable dose.
4. BCIs: methods and approaches for tailor-made neural systems.
5. Neuroimaging biomarkers: identifying the imaging markers useful in the
early detection of the disease or in following up those treatments or therapies that are being offered to the patients.
6. Personalized neurorehabilitation: the competency of establishing and
passing such rehab charts due to the evaluation of brain functions and
behavior.
7. AI in neurology: applying AI not only in the primary inherent big data
analysis but also in the prognosis of that concrete individual treatment
plan.
The concept of individualized patient healthcare professional treatment in neurological science relates to increasing the probabilities of enhanced handling, recovery, and patient satisfaction with their treatment regimen tailored to suit the specic
needs of the patient.
Computational neurology involves computational methods and applying mathematical formulations to understand and become involved briey in model neurological processes. These methods and models offer different types of perspectives
and tools to analyze and understand the complex dynamics of the nervous system
from the standpoint of computational neurology. Some methods are explained in
Table 2.1 [16].
TABLE 2.1
C
dV
dt
hI
t()
()
−
C
dVdtVE
R
m
,
C
m
I
ion
I
ext
I
ion
g
()()
()
=−+−+−gg3,
I
ionNakL
g gg, ,andNa KL
E
Na
E E
KL
,and
m hn,,and
R
m
E
L
V
th
∑∑
E
ss
ij
i
ii
1
.
∑∑
<
,
E
ij
ii
i
s
i
w
ij
−
Ps
eT
Z
Es()/
Computational Neurology Methods
1. Neuronal models
• Hodgkin−Huxley model: This model describes how any potential
action in neurons is initiated and propagated. It uses a set of
differential equations to model the membrane potential V(t),
ionic current, and gating variables:
• Leaky integrate-and-re (LIF) model: This is a similar model that
describes a neuron’s membrane potential V
input currents:
IVnm
mion ext
(, ,,)=− +
27 Brain Challenges and Opportunities in Computational Neurology
with leakage and
L
+
It
is the total ionic
is
where
is the membrane capacitance,
current, and
=−
m
is the external current. The ionic current
given by:
NamhVE Kn VE LV E
where
are the conductance’s of sodium,
potassium, and leak channels, respectively, and
4
,
are their reversal potentials. The gating variables
follow their own differential equations.
resistance and
is the resting potential. If V reaches a threshold
is the membrane
, the neuron “res” and the potential is reset.
2. Network models • Hopeld network: This type of recurrent neural network serves
as an associative memory system. The energy function
for a
Hopeld network with binary units can be expressed as:
=− +
ws
ij ij
2
θ
• Boltzmann machine: This is a stochastic recurrent network that
models complex probability distributions. The energy of a
states is:
=− −
swss bs
()
ij ij
where
are the binary states,
are the weights, and i are the
thresholds. The network seeks to minimize this energy function
to converge to a stable state. i are biases. The probability of a
state is given by the Boltzmann distribution:
=
()
where
is the temperature and Z is the partition function.
,
(Continued)
28 Computational Intelligence Algorithms
x t
()
X f
()
ˆ
.X
−ˆ
x t
()
∞
wa
,
−∞
I XY
(;
pxy
;
˜˜
()
(,)
.I
,
()
x˙ y˙
()
˜
˜°
ˆL()
˜
=−
t
+1
t
˜
()
dV
V
3
−+
WI
dt
3
dW
(
)
,
dt
,,a and b
TABLE 2.1 (Continued)
Computational Neurology Methods
3. Signal processing
• Fourier transform: It is used to analyze the frequency
components of neural signals. For a signal
transform
• Wavelet transform: Wavelet transform provides time-frequency
representation of signals. Continuous wavelet transforms (CWT)
of a signal
is:
= ()
f xte
()
˜
with wavelet function
− j
2ˇ f
is:
t
dt
, its Fourier
4. Information theory
5. Optimization and learning
6. Neurodynamic
x
where
is the scale parameter and
• Mutual information: This measures the amount of information
obtained about one random variable through another. For random
variables Xand
• Gradient descent: This optimization method is used in training
neural networks. For a loss function
where ὴ is the learning rate and L
function with respect to parameters
• Fitzhugh−Nagumo model (FNM): This model is a simple version
of Hodgkin−Huxley model that captures the essential dynamics
of excitable systems:
where
and
, mutual info
=
XY
(
)
is the membrane potential,
are parameters controlling the dynamics.
˜
ˆa a
pxyl
=−
V
=
+ − bW
Va
1 tb
= xt()
( ,b)
−
˘
°
is the translation parameter.
) is:
t
.
dt
*
og
px py
()
, the update rule is:
t
,
is the gradient of the loss
is the recovery variable,
2.6 CONCLUSION AND FUTURE DIRECTION
Computational neurology has the potential to revolutionize the elds of neuroscience and neurotechnology by providing a deeper understanding of the brain
and its functionalities. It uses models like mathematics to simulate neuroprogressive processes. Researchers can gain insights into the underlying mechanism of
29 Brain Challenges and Opportunities in Computational Neurology
neurological disorders, like Alzheimer’s, Parkinson’s, and epilepsy. This knowledge can be used to develop more effective treatments and interventions for these
conditions.
Moreover, computational neurology plays a crucial role in the development of
BCIs, which help to improve the quality of life for individuals with severe motor disabilities. Additionally, computational neurology will help to build neural prosthetics,
such that devices will restore lost sensors and functions by directly interacting with
the nervous system. These prosthetics can give a good change in people who are
suffering from spinal cord injuries or any other neurological diseases. Early diagnosis of neurological disorders, BCIs, and neurofeedback training improve skills and
neurostimulation therapies and drug developments and use of AI in computational
neurolog y.
REFERENCES
1. Lytton, W. W. (2002). From Computer to Brain: Foundations of Computational
Neuroscience. Springer Science & Business Media.
2. Wong-Lin, K., McClean, P. L., McCombe, N., Kaur, D., Sanchez-Bornot, J. M.,
Gillespie, P., … & McGuinness, B. (2020). Shaping a data-driven era in dementia
care pathway through computational neurology approaches. BMC Medicine, 18,
1–10.
3. Leaman, R., Khare, R., & Lu, Z. (2015). Challenges in clinical natural language processing for automated disorder normalization. Journal of Biomedical Informatics,
57, 28–37.
4. Simonyan, K. (2013). Deep inside convolutional networks: Visualising image classication models and saliency maps. arXiv preprint arXiv:1312.6034.
5. Patel, R., Vaghela, R., Chopade, M., Patel, P., & Bhatt, D. (2021). Integrated neuroinformatics: Analytics and application. In Knowledge Modelling and Big Data Analytics
in Healthcare (pp. 133–143). CRC Press.
6. Bisset, K. R., Chen, J., Feng, X., Kumar, V. A., & Marathe, M. V. (2009, June). EpiFast:
a fast algorithm for large scale realistic epidemic simulations on distributed memory
systems. In Proceedings of the 23rd international conference on Supercomputing (pp.
430–439).
7. Prajapati, R., & Emerson, I. A. (2022). Construction and analysis of brain networks
from different neuroimaging techniques. International Journal of Neuroscience,
132(8), 745–766.
8. Bassett, D. S., Khambhati, A. N., & Grafton, S. T. (2017). Emerging frontiers of neuroengineering: A network science of brain connectivity. Annual Review of Biomedical
Engineering, 19(1), 327–352.
9. Hetling, J. R. (2008). Comment on ‘what is neural engineering?’. Journal of Neural
Engineering, 5(3), 360.
10. Durand, D. M. (2006). What is neural engineering?. Journal of Neural Engineering,
4(4), E01.
11. Budman, E., Deeb, W., Martinez-Ramirez, D., Pilitsis, J. G., Peng-Chen, Z., Okun,
M. S., & Ramirez-Zamora, A. (2018). Potential indications for deep brain stimulation
in neurological disorders: An evolving eld. European Journal of Neurology, 25(3),
434–e30.
12. Tan, L., Jiang, T., Tan, L., & Yu, J. T. (2016). Toward precision medicine in neurological
diseases. Annals of Translational Medicine, 4(6), 104.
30 Computational Intelligence Algorithms
13. Charkhkar, H., Cuberovic, I., Dorval, A. D., Tyler, D. J., Welle, C. G., Widge, A. S.,
& Zariffa, J. (2019). Neural engineering: The process, applications, and its role in the
future of medicine. Journal of Neural Engineering, 16(6), 063002.
14. Jain, K. K. (2005). Personalized neurology. Personalized Medicine, 2(1), 15–21.
15. Langanke, M., Brothers, K. B., Erdmann, P., Weinert, J., Krafczyk-Korth, J., Dörr, M., … &
Assel, H. (2011). Comparing different scientic approaches to personalized medicine:
Research ethics and privacy protection. Personalized Medicine, 8(4), 437–444.
16. Kass, R. E., Amari, S. I., Arai, K., Brown, E. N., Diekman, C. O., Diesmann, M., …
& Kramer, M. A. (2018). Computational neuroscience: Mathematical and statistical
perspectives. Annual Review of Statistics and its Application, 5(1), 183–214.
Challenges and
3
Opportunities in
Computational
Neurology
S. Vijayanand and C. Priya
3.1 INTRODUCTION
The human brain is one of the most complex systems in the known universe, consisting of approximately 86 billion neurons that form a vast network of trillions of
interconnected synapses [1]. This intricate biological circuitry enables our thoughts,
perceptions, behaviors, emotions, and the very essence of consciousness itself.
Unraveling the mysteries of how the brain processes information, learns, stores
memories, and gives rise to the richness of human experience has been one of the
greatest scientic challenges humanity has undertaken. Computational neurology,
also known as computational neuroscience, is an interdisciplinary branch of science
that integrates knowledge of neuroscience, computer science, physics, mathematics, and other sciences to develop and utilize computational models and simulations in studies on the structure and functions of the brain and nervous system [2].
Computational neurology researches the basic principles and mechanisms driving
neural computation, cognition, and behavior by incorporating insight from strong
computational methodologies and a wide variety of disciplines. During the last few
decades, the exponential increase in computing power was complemented by rapid
advances in neuroimaging technologies and access to large-scale neural data to bring
computational neurology to the forefront in brain research. Sophisticated computational models and simulations have provided insight into the complex dynamics of
neural circuits, the representation of information in the brain, and neural correlates
for several cognitive functions previously unmatched [3].
Yet, along with these impressive achievements, the key challenges to computational neurology come from the complexity of the brain and from the limitations
of models and methodologies at our disposal. Activities of the brain span a very
wide range of spatial and temporal scales, from the nanoscale of molecular and
ionic interactions to the macroscopic scales of the organization of brain regions and
networks [4]. Capturing this multiscale nature of brain dynamics within a unied
computational framework still constitutes one of the major challenges. Besides, the
brain is highly plastic and able to adapt; it keeps readapting and reorganizing its
neural circuits due to environmental input, learning, and experience throughout its
DO I: 10.1201/ 97810 03520 34 4 - 4
31
32 Computational Intelligence Algorithms
life. This dynamic and ever-changing nature of neural computation, in turn, presents
another level of difculty in developing computational models able to represent and
account for it with precision.
This chapter will touch on some of the most important challenges pending in
computational neurology, including, but not limited to, the problem of the complexity of the brain; the limitations of current data acquisition techniques; issues with
model validation; the integration of disciplines and methodologies; and the interpretability of complex computational models. Moreover, we shall discuss exciting
opportunities and possible future directions awaiting us as we continue to expand
the boundaries of brain research using computational approaches. In returning to
these challenges and capitalizing on the newest achievements in machine learning,
articial intelligence, neuromorphic computing, and brain−computer interfaces
(BCIs), computational neurology could evoke nothing but breathtaking discoveries that may not only substantially advance the understanding of the brain but also
enable the development of radically new applications in such areas as precision
medicine, enhancement of cognition, and building smart systems inspired by biological intelligence [5].
What is now required is interdisciplinarity, sharing not only knowledge and
insight but embedding these various standpoints and methodologies within one great
endeavor. It is only by serious effort across disciplines that the secrets of the brain
will be unwoven and the full power of computational neurology unlocked. The major
challenge in the eld of computational neurology is to take care of the huge complexity of the brain along with the various spatial and temporal scales. The brain contains
almost 86 billion neurons at the microscale, with each making several thousand
connections with other neurons through synapses. Indeed, the work from this dense
interconnectedness gives rise to the amazing ability of information processing by the
brain. Similarly, continued development in computational models is put into practice
in order to simulate the dynamics of the brain from higher to ner scales. The recent
editorial publications published bring out how cognitive function−simulating models and mental disorder−simulating models facilitate grasping not only normal brain
activities but also pathological states like schizophrenia and depression [6]. However,
even the most negligible percentage of neural circuitry in the brain involves computationally intensive tasks to model and simulate. A single neuron itself is a highly
complex computational machine that integrates and processes incoming signals
through detailed electrochemical dynamics. The modeling of behavior by billions of
interacting neurons, all singular in their properties and again singularly connected
with others, quickly becomes computationally infeasible for even the most powerful
supercomputers.
The brain also acts over a huge range of spatial scales, from the nanoscale interactions of molecules and ions to the macroscopic organization of brain regions and networks. It is a big challenge to capture the multiscale nature of brain dynamics using
current computational models, which tend to focus on one particular scale or level
of abstraction. The temporal complexity of brain activity also poses a formidable
challenge. Neural computations occur across a wide range of timescales, from the
millisecond dynamics of action potentials to the slower processes of synaptic plasticity, learning, and memory consolidation that unfold over hours, days, or even years.
33 Challenges and Opportunities in Computational Neurology
Bridging these disparate timescales within a unied computational framework is an
area of active research.
3.2 CHALLENGES IN COMPUTATIONAL BRAIN MODELING
3.2.1 COMPLEXITY OF NEURAL NETWORKS
The human brain encompasses approximately 86 billion neurons, each forming connections (synapses) with thousands of other neurons in incredibly dense,
recursively networked architectures [7]. The total number of synapses is estimated around 100−500 trillion. This vast, heterogeneous connectivity gives rise
to the brain’s prodigious information processing capabilities. However, the sheer
combinatorial complexity makes modeling entire brain networks a grand challenge for computation. Current neural simulations are highly simplied compared to biological reality, often abstracting away much of the intricate biological
details [8]. Capturing the dynamic, nonlinear interactions of such an immense
network is extraordinarily difcult, requiring massive computational resources
and novel modeling approaches. Even modeling a small fraction of the brain’s
neural circuitry is a formidable task. For instance, the Blue Brain Project’s reconstruction and simulation of a rat cortical microcircuit, comprising around 31,000
neurons and 37 million synapses, required a supercomputer and took years of
effort. Scaling such detailed simulations to the level of the entire human brain,
with its billions of neurons and trillions of synapses, remains an immense computational challenge.
3.2.2 BRIDGING MULTIPLE SCALES AND MODALITIES
Brain functions and neural coding emerge across multiple spatial and temporal
scales, from molecular events within synapses, to neuronal membrane potentials, to
network-level oscillations, to systemwide cognitive functions. Bridging these scales
in unied computational models is extremely complicated since each scale involves
different types of data, theories, and modeling approaches. For example, biophysical models simulate neurons as multicompartment structures based on cable theory,
while cognitive models use systems of interacting units approximating brain areas or
functions. Integrating bottom-up molecular data with top-down cognitive constraints
in a neurobiologically constrained manner is a key challenge [2]. Additionally, the
brain exhibits a wide range of dynamics across different timescales, from the millisecond dynamics of action potentials to the slower processes of synaptic plasticity, learning, and memory consolidation that unfold over hours, days, or even years
[9]. Capturing these disparate timescales within a unied computational framework
remains an open challenge in the eld.
3.2.3 LIMITATIONS OF NEURAL DATA
Despite signicant development in multimodal neuroimaging and neural recording
techniques using fMRI, PET, EEG, calcium imaging, and multielectrode arrays, at
Соседние файлы в папке Библиотека им академика М.И. Перельмана